Medium Hot

First published by Verso 2025

© Hito Steyerl 2025

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ISBN-13: 978-1-80429-802-2

ISBN-13: 978-1-80429-804-6 (UK EBK)

ISBN-13: 978-1-80429-805-3 (US EBK)

British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

Library of Congress Cataloging-in-Publication Data

Names: Steyerl, Hito, author.

Title: Medium hot : images in the age of heat / Hito Steyerl.

Description: London ; New York : Verso, 2025. | Includes bibliographical references.

Identifiers: LCCN 2025001853 (print) | LCCN 2025001854 (ebook) | ISBN 9781804298022 (hardback) | ISBN 9781804298053 (ebook)

Subjects: LCSH: Art and technology—History—21st century. | Art and society—History—21st century. | Artificial intelligence—Environmental aspects. | Artificial intelligence—Moral and ethical aspects.

Classification: LCC N72.T4 S74 2025 (print) | LCC N72.T4 (ebook) | DDC 701/.05—dc23/eng/20250206

LC record available at https://lccn.loc.gov/2025001853

LC ebook record available at https://lccn.loc.gov/2025001854

Introduction

A preliminary description of capitalism in the age of AI might be: computational power plus bureaucracy. Bureaucracy is the base of statistics. There is no machine learning without forms and spreadsheets. Add massive computational power, and the extracted data are made to perform. However, the age of performative statistics has just begun. Therefore, all descriptions are preliminary.

The conjunction of statistics and heavy data processing also updates twentieth-century ideas about images. Statistical data renderings – a term which most precisely describes the visual output of generative AI (GenAI) image apps like Midjourney, Kling, Grok and the like – are probably something both more and less than images.¹ They use ground-up data and apply financialised formulas (among others) to create probability-based patterns. The result is a subprime visibility prone to speculation and crashes. The massive infrastructure required for this process comes with its own political economy, built around extractive monopolies and requiring considerable amounts of energy and resources.

The emergence of GenAI renderings also coincides with an era of widespread multi-crisis – with regular financial breakdowns the default setting, post-pandemic recessions, accelerating climate change and increasing extreme weather events, rising right-wing and neofascist movements globally, and several active military conflicts with high numbers of civilian casualties in Yemen, Ukraine, Tigray, Gaza, Sudan and Congo. Many of these elements are interlinked and interdependent to a high degree – an ironic fact in view of an industry that tries hard to create ‘autonomous’ applications. The more machine ‘autonomy’, the more entangled and messed up the world seems to be, an egregious Rube Goldberg machine in which almost everything impacts everything else. The size of a swastika leg impacts some methane fart correlated to some scam crypto-investment that increases destitution somewhere else. The rapid deployment of GenAI happens within a moment of transition towards a brittle multipolar world order, driven by platform authoritarianism, in which tech evangelists incongruently call for an even faster acceleration of entropy. The more predictable things become through so-called AI, the more out of control they get. There is a rational explanation for this: hedging future risk creates present-day trouble. In other words, the more one tries to pre-empt the future, the more the present gets out of hand.

In 2015, I unknowingly witnessed an important milestone in this kind of technological constellation. I was at Mount Korek, a peak in the semi-autonomous Kurdish Region of northern Iraq, to film an abandoned and partly destroyed observatory. The drone we used to film this location, however, carried equally interesting stories. At the time, there were only three consumer video drones in the KRG. One was operated by a person who had learned his craft in a mountain guerilla film school with the Kurdistan Workers’ Party (PKK); another was used by Daesh to make propaganda videos. That same year, Daesh started using consumer drones fitted with explosive loads in kamikaze attacks on Kurdish forces. One year later, the self-proclaimed Islamic State announced the foundation of an organisation called the ‘Unmanned Aircraft of the Mujahideen’. Most interesting, for our purposes, is the bureaucratic layer of this operation. Documents found in Mosul show a type of organisation that institutionalised the collection of data through a heap of administrative documents, permission forms, receipts, drone use reports and spreadsheets. Receipts show the acquisition of DIY drone parts and hobby drones.²

In the same bureaucratic vein, a ‘network of shell companies across 5 different countries’ was set up ‘to buy gear from manufacturers in Asia, the US, and Canada using sites such as PayPal and fake aliases’.³ Daesh bureaucracy thus became one of the building blocks of AI-powered weapons development. Just a year later, their Kurdish opponents followed suit, using fixed-wing models like the US$115 Talon X-UAV to attack Turkish forces repeatedly. Turkish forces responded by developing a highly successful drone programme, including the infamous Bayraktar TB2.⁴ Eventually, Turkey became the first nation to deploy a fully autonomous AI weapon. According to a UN report, the attack was carried out in Libya in March 2020 by a Kargu-2 quadcopter drone (manufactured by the Turkish company STM) ‘during a conflict between Libyan government forces and a breakaway military faction led by Khalifa Haftar, commander of the Libyan National Army’. The report notes: ‘The lethal autonomous weapons systems were programmed to attack targets without requiring data connectivity between the operator and the munition: in effect, a true “fire, forget and find” capability.’⁵

The development of autonomous weapons has since been massively accelerated through the conflicts in Gaza and Ukraine – places which have become laboratories for testing AI warfare, very often by Western arms makers looking for a robust and regulation-free R&D environment.

In the situation unfolding around Mount Korek in 2015, three areas converged that would became crucial for the further development of machine learning–based image generation: sensing technology, conflict and a specific type of deep bureaucracy oriented towards the extraction and management of data.

Fire, Find and Forget: The Question of Autonomy

In this process, the traditional relationship of image and conflict changed. Until this time, visual data had been making warfare, surveillance or production more efficient, in the guise of so-called operational or programmable images. Operational images actively engaged in actions of any kind, as Flusser and Farocki, Volker Pantenburg, and Jussi Parikka have argued.⁶ One side effect was that a lot of these data became unintelligible to human senses – invisible, or rather, in-visual data constellations accessible only to machines.⁷ All sorts of data were drafted into operations – as means to an end.

However, now it almost seems as if warfare is increasingly being drafted into data generation – as if the production of military data were at least as important as war as such. Why is this necessary? Military activities are a ready-made component of an artificial general intelligence (AGI) development pipeline touted by major data corporations. The development of so-called world models – supposedly able to reason and exhibit human-like capabilities in some areas – requires massive amounts of data input in order to condition networks on so-called ‘machine common sense’ and ‘intuitive physics’.

The generation of visuals (for example, massive amounts of drone footage or driving videos, real or simulated) is just one necessary next step towards the development of bigger models. War is an optimal scenario for collecting the necessary information. Conflict also helps to create an exploitable AI workforce, as the case of microworkers displaced by violence or oppression demonstrates.

This draws attention to the fact that the nature of labour may have partly changed as well. The phrase ‘operational image’ refers to an image that ‘works’. The Italian word opera means ‘work’, both in the sense of the labour done and the result produced. During the digital disruption of the 1990s, as post-workerist theorists have argued, classical ‘material’ labour was being supplemented by ‘immaterial’ or ‘virtuoso’ cognitive labour. During this period, labour also turned into a performative activity.⁸ However, today, immaterial labour is being automated as well, giving rise to an automated performance of labour. Statistical renderings are automated performances of data, a sort of stochastic ballet méchanique of pixels and features in latent space. Probabilistic systems are now assuming the role of post-Fordist virtuosos that are supposed to be creatively on fire 24/7. If labour as performance was tied to the idea of ‘always working’, its automation means that for many humans, ‘never working’ may be a new reality. Machines take over the performative part of labour as well as – and more importantly – the earning part, in a process that edges out simple automation with increasing autonomisation.

Operational images are thus upgraded to data performance events. Rather than images, they might constitute da/mages – data/images wrecking their surroundings through the extraction of labour, data and resources, rendering warfare, marketing and surveillance as variations on the same spectrum. Each one of these creates some kind of damage or partial destruction of reality.⁹

Art as Pretext

How does it impact on the work of making images, or for that matter art, when image-making is inseparable from da/mage-making? How does the creation of autonomous weapons impact on so-called autonomous art?

At present, art (or creative labour) seems to be used as a pretext to provide meaning or purpose to the top-down implementation of GenAI tools and their inclusion in all sorts of digital soft-and hardware. There are most certainly valid scientific use cases for machine learning–based tools, as there are for statistical operations more broadly – anything helped by brute force computation of large datasets, data analysis and pattern recognition. Valuable insight into climate or medical science questions might be gained. Perhaps computational statistics might even help bring about a more just reallocation of resources in societies – though this is currently extremely unlikely given the interests of the people controlling the latter.¹⁰

Even within image generation, some GenAI tools could be useful, perhaps even interesting, were they not so embedded in systems of expropriation and waste.¹¹ This could theoretically change through the development of less compromised and fossil-dependent infrastructures including open-source models built on sustainable data acquisition – though this might also create quite severe problems in the guise of the proliferation of weaponisable technology.

But, apart from these activities, is there any type of general usage that might justify the rollout of resource-intensive AI infrastructure on the vast scale seen in recent months?

The integration of machine learning tools into search engines, for example, increases their energy demands by six to ten times, and data centres have apparently been consuming up to seven times more energy than officially reported.¹² As a result of missing its CO₂ emission goals by 50 percent, Google stopped considering itself carbon neutral. The AI tech stack requires the construction of a large-scale infrastructure, including power supply and logistics chains for resources like data centres, chips, computation and more – guzzling up resources, destabilising societies and increasing the gap between upper and lower classes, with hardly anything left in between. How compression and optimisation technologies (seen with recent Chinese developments) change this situation is still an open question.

However, as of now, the reason for this statistical blitz is quite unclear. Economist Daron Acemoglu predicts overall productivity gains caused by AI to be ‘no more than a 0.66% increase in total factor productivity (TFP) over 10 years’. The factor that might increase, in his view, is the gap between capital and labour income.¹³ Until now, corporations largely fail to monetise AI products, enormous investments notwithstanding. A so-called ‘killer app’ is nowhere to be seen. For now, GenAI’s missing ‘killer app’ is obviously the traditional killer app: weapons. In the mind of the AI industry, war becomes an opportunity to get hold of tax money as part of a national defence effort, in the form of an all-out arms race towards AGI.¹⁴

‘Creativity’ as Pretext

This situation provides the larger context for the hype around so-called ‘AI creativity’, the ‘democratisation of arts’ and other slop slogans. Artists somehow have to provide the missing rationale for the AI hype and present the happy face of (re-)armament, so to speak, while creative production is simultaneously being partly automated.

Especially since the release of image generators like Stable Diffusion, DALL-E, or Pika AI, a torrent of ready-made PR mythologies has frantically celebrated the automation of all sorts of creative and semi-creative tasks, from the making of literature and images, to sound creation, to almost any other activity that can be done on a computer. The project, as proclaimed by the industry, is the democratisation of art and ‘creativity’ and increased ‘access’, echoing the crash-landed sales pitches of the crypto-based boom in so-called non-fungible tokens (NFTs) around 2021 (which relied on phrases like ‘eliminating gatekeepers’).

The mission of the PR onslaught promoting machine creativity, emerging capabilities and machine sentience is to somehow distract from the fact of its military implications, as well as the more inconvenient topics of imminent technological unemployment for many and its unpredictable add-on effects on energy supplies, inflation and related social instability – for example, rising fascism. Professions that seemed relatively immune to automation until now suffer ‘autonomisation’, which one might tentatively define as automation plus ‘creativity’ or seeming ‘independence’. Photography, translation, web design, game design, 3D work, advertising, digital post-production, stage design, PR and even programming are disrupted, if not decimated outright.

Not so long ago, many of the newly obsolescent were lured into those professions by the neoliberal promise of ‘creative industries’. The ability to conjure up JPEGs of ‘cats with hats’ by means of magic incantations (so-called prompts) is supposed to dazzle away the multiple problems, as another set of tech monopolies hijacks access to public utilities like information and communication, siphons off people’s data and sells them back the products wrested from their own expropriation.

Firsthand Reports

This collection of essays documents some episodes in the rapid expansion of so-called AI into all sorts of creative professions, including mine – namely filmmaking. These texts are not to be confused with academic papers or academic writing – a style particularly suited to replication by large language models because of its easily reproducible normativity and structural conformism. Make no mistake though: no style is immune to automation/autonomisation. At the end of the day, the so-called general intellect has mostly been ingested and made proprietary by large-scale models already. The frontier has now shifted to more multidimensional types of information, especially those concerning orientation and movement, to somehow mimic ‘embodiment’, with the aim of breaking through the screen to begin the full-scale colonisation of 3D space.

Many texts in this volume are first-hand reportage from the practice of making images in the period between 2017 and 2024, when so-called GenAI (and blockchain technologies) exploded onto the scene. Most of them were written while waiting for some visual to render or train, or while slogging through endless error messages and Stack Overflow forum advice, trying to figure out the dusty and pedantic world of statistical image production from within. Some are ruins of artistic attempts cancelled by the COVID-19 pandemic. The essays document debates that took place as machine learning began to make swift strides in the second decade of the twenty-first century: from the goofy and genuinely surprising hallucinations of Google’s DeepDream (2014) to the simultaneous stealth spread of artificial stupidity in the guise of bot armies, Amazon wristbands for monitoring the performance of fulfillment-centre employees, or social security systems made to fail on purpose.

The key breakthroughs in AI-imaging technology in this period are neither the 2014 development of generative adversarial networks (GANs), nor the invention of the extremely impactful ‘transformer’ architecture in 2017, which would unleash its potential a little later. Rather, the key milestones of machine learning technology in this period were more prosaic in nature. In 2019, the Dutch government deployed a self-learning algorithm to presumably spot child benefit fraud that ended up racially targeting many completely innocent families, pushing them into debt and poverty; some even committed suicide. Another milestone was the many protests triggered by GenAI tools after 2022, addressed below in ‘Medium Hot’ (written largely in summer 2023).

No other recent technology has caused as many strikes, lawsuits or online campaigns – which, it must be said, have at least succeeded in making the automation of creative technologies vastly less sexy. Meanwhile, the enormous environmental toll of AI (as well as many blockchain) industries has become increasingly more evident, as considered in ‘Burnt-Out Images’ (2023–24), ‘We Didn’t Start the Fire’ (2021, with Gago Gagoshidze and Miloš Trakilović) and ‘The Digital Rift’ (2024). Other texts in this volume focus on GenAI’s continuity with previous extraction and surveillance practices from phrenology to statistics (‘Mean Images’, 2022), to so-called crypto-colonialism (‘Common Sensing’, 2023). ‘Knuckleporn or Phocomelia’ (2024) probes specific AI models, while ‘Walk the Walk’ (2021) reflects on the supposed promises of the blockchain for artists, which imploded only months later.

Another essay looks at creepypasta from after the 2016 Trump election: a tale called ‘Roko’s basilisk’ that keeps informing contemporary Lovecraftian AI lore like so-called ‘long-termism’ and ‘existential risk’ mythology. There is even one text written in collaboration with the large language model GPT-3, ‘Twenty-One Art Worlds’ (2021, with the Department of Decentralization), when this could still be a somewhat fun activity – a time when corporate LLMs still were able to produce weirdness instead of optimised conformism. Throughout these investigations, I have encountered brilliant professionals – a number of them engineers concerned about the consequences of their activities, but also all sorts of other people affected by the turmoil created by technological and social unrest and disruption.

The essays throw the spotlight on some aspects of the swift proliferation of machine learning–based technologies and their impact on society, art and different types of labour. Perhaps they will become time capsules for future historians, who might at some point ask themselves: What did people think when all this started? How did they deal with the threat of their own zombification, that is, of being made obsolescent or undead by tools of statistical conformism? At which point were people modeled as noise – as suspended social particles bombarded by chaotic Brownian forces and mercilessly tossed around? What was the temperature of debates at that point?

Perhaps someone will want to retrospectively reconstruct the period of yet another technological revolution at the cusp of the twenty-first century, within a period of widespread instability, climate change, precarity and above all a sense of shattering of what Hannah Arendt called a ‘world sense’. In her view, world sense emerges from a shared sensus communis (which roughly translates to ‘common sense’). It involves a basic sense for reality and orientation – something AI industries are presently working hard to integrate into their models. Meanwhile, Arendt’s world sense has already been undermined by the infodemics of Web 2.0 – a multiverse of fake news animated by mobbing-cum-self-branding. World sense has also been eroded by the emergence of internet zones that overlap with new multipolar political realms kept strictly separate from one another.

Marshall McLuhan’s idea of a global village has been copy-pasted into 3D space: as a collection of worldwide villages that are increasingly insular and parochial; are defined by mean anonymous gossip, censorship and repression, and the exclusion and cancellation of others; and which act increasingly hostile towards one another. The idea of the world has been deprecated, for better or worse. It is also possible that in the future, history writing itself will be redefined by machine learning technology to become something entirely different.

As events have only just begun to unfold, most of the important questions remain open; indeed, many cannot even be formulated yet. Things are moving so quickly that the swift obsolescence of any current ideas about them is unavoidable, no matter how hard anyone tries to follow up. This also means that many current questions may soon appear quaint, or already will by the time the book is published. In the age of autonomisation, what happens to earlier concepts of autonomy? Will autonomous art unfold as an autonomous art by machines for machines? What is a ‘fire, find and forget’ function for artworks, and what is the audience for such performances?

What happens to the aesthetic and social implications of so-called common sense once it is captured by stochastic systems and populist politics, and what does this tell us about the original limitations of this idea of aesthetics? What about the meaning of ‘meaning’, when it is handed over to opaque and proprietary systems? How to stop the social and environmental damage created by thermodynamical systems of image-making? How can one disrupt the arms race and existing development and deployment of AI-based weaponry? And what happens to human systems, and humans themselves, when human thought and sensibilities become expendable due to the increasing autonomy of machine systems?

1 ‘The Hardest Part’: Statistical Image-Making and War Economies

In 2022, several so-called AI image generators were released to the public. After DALL-E by OpenAI, Midjourney and Stable Diffusion were unveiled over the course of the year. The new generators could render illustrations from text input – so-called prompts – something previously unheard of. This unleashed major media hype, which led to a massive surge in usership for all the companies involved.¹

In October 2022, video generators started to be released. The amount of discussion and attention produced by these new tools was breathtaking. Discussions about the future of art and automated creativity created huge amounts of complimentary PR for the startups involved, pushing the idea of a new economic cycle fuelled by general-purpose AI into view of the public. Art became a perfect territory for discussions about AI. It distracted from the fact that, actually, not even the companies themselves knew exactly what general-purpose AI could conceivably be good for, except for automating marketing and text and media generation.

At this point, there wasn’t much other purpose to generative AI than to privatise the whole history of art, package it up, and sell it back to its still-living creators in the form of models that would partly automate their tasks. The release of the generators, unsurprisingly, sparked a huge backlash from artists and creators, including several waves of protest and lawsuits.² Other artists, however, started exploring the new possibilities – accidentally shedding light on some important dynamics of how so-called AI might evolve.

A case in point is ‘The Hardest Part’, one of the first music videos made with Sora, a much-hyped video generator by OpenAI. It was directed by Paul Trillo for the band Washed Out. Access to Sora was, and still is (as of June 2024), very limited, and Trillo was among only a few invited to test the new tool – prompting references to those excluded as ‘Sora losers’.

What can one say about this video, and whatever it may indicate about Sora? What does this video say about the state we are in right now? What kinds of social tensions does it express, condense or repress? And why are its protagonists running so fast?

Fly-By in a Blur

First: the video gets its job done in a perfectly viable manner. It is a dynamic collage of shots moving forward through various environments at high speed, apparently following a couple (or its AI derivatives) as they run towards the background. Generator morphing is apparent, but perfectly standard editing techniques are also deployed, which break the illusion of a continuous tracking shot. The video also looks as if it could just as easily have been created on some other generators, which have been live for a while and are perfectly accessible to everyone – even ‘losers’.

At second glance, bearing in mind the evolving character of this technology, other things come into focus. In comparison to Sora’s carefully curated demos a few months earlier, the generated imagery in this video looks rather pedestrian. Nonetheless, the threat of job automation is non-trivial. For example, when the first Sora demos were released, producer Tyler Perry put plans for a US$800 million studio expansion on hold, citing concerns it might be rendered obsolete because video creation would be automated anyway by generative AI.³

That threat is still very valid, but not because of any breakthrough featured in ‘The Hardest Part’. Though progress is swift, there are no shocking technological improvements here – surely nothing that imminently threatens to lay off thousands of film professionals. Both the pace and the POV of the camera, which follows main characters from behind, are likely reflections of well-known current AI generator issues. Even when generated by Sora, faces do not always turn out well, nor do human movements. The camera’s placement behind the main protagonists and its speed of movement, so quick that everything else flies by in a blur, are an effective way to gloss over those features. The same goes for temporal limits: tools like Sora cannot generate lengthy stretches of footage, hence the cuts. Still, those issues are generally improving, and Sora fares better than other generators in producing plausible human movement.

However, it is fair to conclude that the video’s depiction of these figures from behind, and running quickly, serves to avoid exposing persistent deficiencies in generative AI’s representation of humans.

A Race to Be Won

Is this the only reason that the protagonists in the video are running so fast? Here’s another attempt at an explanation: they run fast because otherwise someone else will win the race.

This is also why the video exists in its current form in the first place. Trillo, the director, explains his rationale for making the video: ‘Was there going to be someone else that makes the first Sora music video, if we didn’t? That would have happened.’⁴ This means he made it because otherwise, someone else would have beaten him to it. The video was going to be made no matter what; it was just a question whether he was going to get the publicity or someone else. Currently, this sort of pre-emptive decision-making seems to reflect broader trends in AI.

The sprint in ‘The Hardest Part’ can, for example, be interpreted as a literal depiction of the race for AI – or, more precisely, an artificial general intelligence (AGI) / artificial superintelligence (ASI). The protagonists are in such haste because there seems to be no alternative – because otherwise someone else would win.

At least this is one of the underlying arguments in ‘Situational Awareness’, a treatise on AI that opens with a stark prognosis:

The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be unleashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.⁵

In a few sentences, it maps out the milestones of an AI-based arms race. Since it has already started, there is no way to stop it. AGI, as well as superintelligence, are unavoidable, as is the transformation of anything AI into a question of national security and the military. One might as well build AI-based weapons of mass destruction oneself, before someone else does it. In the opinion of its author, the West has to do precisely that. Along the way, US fracking gas, and plenty of it, needs to be burned to power data centres. The use of fossil energy is a necessary inconvenience. There is no way around it if we are to fuel trillion-dollar gigacluster data centres to manufacture the AGIs that will, in turn, produce superintelligences. The race is on, and the only option is to win.

Who is the person making such predictions, and why am I quoting him in the first place?

In fact, the author of the treatise, Leopold Aschenbrenner, worked at the same OpenAI that developed the Sora video generator. He states that he was fired from OpenAI’s so-called Superalignment team because he expressed safety concerns and leaked a document to AI safety researchers. (He insists there were no company secrets divulged.) ‘Superalignment’ is the name given to OpenAI’s safety process, meant to hedge against risk that AIs will run amok or otherwise jailbreak from their graphics-processing unit (GPU) clusters. This train of thought takes into account so-called existential risk, as articulated in ideologies like long-termism, which worry that the fates of people a billion years down the line could be affected by the development of AGI. Aschenbrenner was very likely immersed in long-termist ideas while previously working at the Future Fund, a philanthropic entity started by former FTX chief Sam Bankman-Fried that aimed to finance projects to ‘improve humanity’s long-term prospects’.⁶

But in Aschenbrenner’s version, ‘superalignment’ is a much more pragmatic concern, having less to do with a future a billion years away than with potential contemporary mayhem and war. He distances himself from long-term considerations, focussing instead on the actual risk of instability and national security threats in the wake of AI disruption – which is arguably a more realistic perspective on this issue. Meanwhile, following his departure from OpenAI, he has also founded an AGI-themed investment company. To paraphrase Paul Trillo: if AGI is going to be developed anyway, one might as well profit from it.

Now, let me make clear: whatever Aschenbrenner is anticipating is very hard to verify. His claims might even be true. After all, proprietary technology and routine black-boxing create a situation in which it is difficult to fact-check Aschenbrenner’s declarations, pronounced from the position of a proud and überconfident insider. OpenAI itself may not know precisely how its systems work in the first place. The fact that Aschenbrenner stands to immediately profit from his own unverifiable statements does not exactly inspire much trust either. Indeed, he may very well be bullshitting his audience all the way. But trustworthiness is not the only issue; there are also many reasons to doubt whether the current AI boom (and hype) will endure. For one, many tech corporations may fail to find viable business models to recoup their massive investments, causing the current AI investment cycle to crash. Also, dependency on energy-hungry Nvidia GPUs will lessen at some point because of chip innovation and algorithmic optimisation. Moreover, training new, ever-bigger models might cause computing infrastucture to hit limits in the availability of power, data or both, and yield diminishing returns. Productivity gains within the general economy might therefore be much smaller than expected. There are many factors which make a simple extension of current growth charts very unlikely. But unfortunately, when it comes to the structure of the race, it doesn’t matter whether any of Aschenbrenner’s predictions are based in fact or will ever come true.

Because if the idea of an unavoidable race takes hold, militarisation, and the creation of a national security–based war economy, will ensue, whether AGI actually will exist or not.⁷ The reason stems not from any actual fact or scientific breakthrough but from competition; to paraphrase Trillo: ‘Was there going to be someone else that makes the first AI weapon of mass destruction, if we didn’t? That would have happened.’ The sheer panic spurred by the thought of someone else winning this race is enough to prompt participation in it. The race to develop those weapons first has already started. And because there will always be another threat of someone else being first, whether it’s a filmmaker, competitor or the ‘CCP’ (as Aschenbrenner imagines) developing AI-powered weapons of mass destruction, the race becomes unavoidable – and must not only be run, but won. Perhaps the race in ‘The Hardest Part’ is reflective of these cirumstances.

The Road Map to War Economies

Aschenbrenner’s vision taps into recent historical experiences of precipitous state-led crisis management, such as post-2008 bailouts or the COVID-19 pandemic, but puts a new twist to it. Tl;dr: he leaves behind neoliberal economic thinking and laissez-faire ideology to argue that a state-led war economy is not only unavoidable but the only institution able to deal with present-day challenges within a multipolar world system.

In a June 2021 podcast interview, Aschenbrenner recounts that he was seventeen years old when the pandemic hit, and that the slow but massive federal reaction in the US – major lockdowns thought unimaginable only weeks earlier – impressed him in scale if not efficiency. For someone who came of age with the outbreak of the pandemic, retail investment, pizzagate and pepecoins, his perspective is quite understandable. Of course, he also shorted the pandemic, which seems to be a generational reflex.⁸ All the while, the memory still lingers of the massive state bailouts of financial industries following the 2008 collapse of subprime debt schemes.

This surprising turn towards the state is echoed in recent popular culture, such as in Christopher Nolan’s questionable 2023 filmic glorification of Robert Oppenheimer’s development of the nuclear bomb. The Manhattan project triggered a huge national-tax-sponsored industrial effort, eventually killing many civilians. Aschenbrenner’s generation thus comes of age within a paradigm break. Their experience tells them that the cult of the invisible hand, of Silicon Valley libertarianism and silly Google playground offices has not been able to deal effectively with recent crises. Not only did nation-states bring on the cavalry in decisive moments during recent financial crashes and a global pandemic; it also became clear that they were the only institutions capable of mounting a response at scale. No longer is this apparatus a laissez-faire liberal state, much less a welfare state. Rather, it converges towards a warfare state and a nationalist war economy.

It is not only to Silicon Valley ‘superalignment’ specialists that the prospect of a war economy occurs. Indeed, when Russia invaded Ukraine in 2022, French president Emmanuel Macron declared that Europe needed an ‘économie de guerre’ – a war economy – to underwrite its security.⁹ The emergence of a war economy is very palpable these days in Germany, where arms manufacturers like Rheinmetall are having a field day thanks to worldwide rearmament. These war economies have been foreshadowed as well as advertised within the art world.

Dimensions

In 2023, a large AI-themed art exhibition called Dimensions was launched in Leipzig, Germany. The organiser was an impresario called Walter Smerling, who had already made headlines a year prior for assembling a show called Diversity United in Berlin, organised by his own association and patronised by the heads of state of France, Germany and Russia. Its last leg opened in Moscow after the Ukraine invasion, which left German president Frank-Walter Steinmeier scrambling to disassociate himself from it.

Diversity United was sponsored by numerous companies with ties to the German–Russian oil and gas trade. It stood in the tradition of a large number of art shows in the scope of the so-called cultural pipeline, a strange collaboration between institutions like Deutsche Bank on the one hand and the Soviet ministry of culture on the other, trying to decorate their mutual interest in fossil extraction from Siberian gas fields with art. The debacle of Diversity United became a symbol for the one-sided dependency of German industries on Russian fossil energy sources. Only one year later, Smerling launched a new venture.

His new AI-themed show Dimensions had a brand-new sponsor: Palantir Technologies, the notorious data analytics company founded by Peter Thiel with the help of an investment fund run by the CIA. Palantir became famous for providing tools for US predictive policing and helping ICE deport undocumented immigrants.¹⁰ And, in contrast to the clearly Russia-oriented Diversity United, Dimensions wasted no time in providing a spotlight for Palantir to advertise its AI weapons as assisting the Ukrainian war effort. Palantir CEO Alex Karp used the attention generated by digital artists such as Refik Anadol and Lu Yang to explain his ideas in several newspaper interviews in the German press.¹¹ Karp expresssed his views, first, that Palantir uses AI in Ukraine in many different ways; second, that AGI will happen, and therefore that Palantir supports a strong and militarised West against other world powers; and, third, that art is supposed to play a big role in this effort. In fact, Karp describes himself as an artist and Palantir as a ‘community of artists’. He upped the ante in a New York Times profile where he posed with a gun on his own shooting range declaring: ‘I am an artist with a gun.’

Palantir’s Studio Practice

A year earlier, in June 2022, Karp had traveled to Ukraine – four months after the start of the Russian invasion. Since then – and with the help of many other actors – the battlefield in Ukraine has been transformed into a laboratory for AI-related military technologies. More than half a dozen Ukrainian ministries are using Palantir’s products, among many other things, to generate military targets. As an article in Time explains:

No human being would be able to compare thousands of images with thousands of hours of intercepted communications. The software does it more or less instantaneously.

Some tranches of the targeting process can be considerably automated. Just as a deep learning algorithm knows how to recognise a picture of a dog after some hours of supervised learning, the Palantir algorithms can become extraordinarily apt at identifying an enemy command and control centre. Presumably, there are hundreds or thousands of indicators for such a target, which can be graded according to relevance. Machine learning algorithms are particularly suited for warfare because their very high number of variables, most invisible to the human eye, make them very difficult to deceive or evade. Nevertheless, the idea that the full cycle can be automated is pretty extreme and still very far from where the technology or doctrine are today.¹²

Ukraine thus becomes Palantir’s war lab – or its studio, to apply Karp’s art-oriented self-description. Gaza may be, too. In 2021, the eleven-day war on Gaza was already described as the world’s ‘first AI war’. In January 2024, Palantir sold an ‘Artificial Intelligence Platform that uses reams of classified intelligence reports to make life-or-death determinations about which targets to attack’ to the Israeli Ministry of Defense.¹³ It is unclear if this system is identical with the ones described in leaks published by Israeli magazines +972 and Local Call.¹⁴

Israeli military intelligence whistleblowers described a data analysis tool used for generating a massive number of bombing targets on the base of correlated phone location data. Those probable targets were – in the case of presumed low-ranking Hamas or Islamic Jihad operatives – attacked once they got home using dumb bombs, surrounded by family and neighbours, even though it was generally accepted that at least 10 percent of all target predictions were wrong.¹⁵ One of the tools is cynically called ‘Where’s Daddy?’

These are just a few examples of how machine learning technology is increasingly tested on the battlefield and how machine learning is deployed to automate war. Obviously Palantir is not alone in this effort. For instance, the Ukrainian AI system Griselda ‘allows for processing a large flow of intelligence information coming from various sources, such as satellites, drones, social networks, media, and even hacked enemy databases. It is noted that “it takes 28 seconds from the time the information appears in the system to the time it is received”.’¹⁶ Other Ukrainian inventions include the deployment of a huge acoustic sensor system, basically consisting of old phones, to monitor any kind of sonic information as well as outfitting small consumer drones with machine vision and bomb loads to maximise battlefield automation.¹⁷ In contrast to Palantir’s rhetorics of high-end technology, locals emphasise DIY approaches using gaming hardware and adapting consumer-grade equipment.

Local officials thus seem sceptical of the quality of Palantir’s art practice: according to one journalist, ‘Ukrainian officials have also criticized some AI wonders promised by tech vendors as “R&D projects”, more hype than impact’.¹⁸ Palantir may, rather, be using the Ukraine war to advertise their tools, just like OpenAI uses Paul Trillo’s video to flaunt Sora and Dimensions artwashes Palantir’s AI-based weapons manufacturing.

Winner Takes All

Let’s return to the beginning and ‘The Hardest Part’, the music video made to advertise OpenAI’s video generator Sora. The protagonist couple is running like crazy, but why are they running in the first place? One of the reasons is certainly to hide Sora’s flaws when it comes to animation and the generation of faces and body movements, and to avoid lingering on misformed bodies for too long. Another may be a reflection of an ongoing AI arms race that overlaps with art in unexpected ways. But there might also be a third option. Why this race in the first place? What is the industry trying to blur by running so fast?

Even in the absence of any actual military component, the AI race is currently structured as a zero-sum game. According to a Guardian explainer, ‘Already, the “frontier” competition is thought to be “winner takes all”, with very little stopping customers from jumping to the latest leader. That means that if one business spends $100m on a training run for a new AI system, its competitors have to decide to spend even more themselves or drop out of the race entirely.’¹⁹ Larger companies have an advantage because they have a lot of training data and can afford talent, necessary investments and construction of infrastructure and data centres at scale. They are therefore racing to create near monopolies as fast as possible, both on a national and international level. This already has real-world consequences, even long before AGIs or weapons of mass destruction are developed.

Google has admitted that the technology is threatening its environmental targets after revealing that data centres, a key piece of AI infrastructure, have contributed to the 48 percent increase in its greenhouse gas emissions from 2019 to 2024. It said that the ‘significant uncertainty’ around reaching its target of net zero emissions by 2030 – reducing the overall amount of CO₂ emissions for which it is responsible to zero – included ‘the uncertainty around the future environmental impact of AI, which is complex and difficult to predict’.²⁰ Microsoft, which handles OpenAI’s model training and deployment, is exceeding its overall emissions target by around 30 percent.²¹ So whatever the goals of the race are – and whether or not they are realistic or make any sense – its effects are already present and real, resulting in an exacerbation of both the climate and energy crises.

All the while, it is clear that ongoing energy crises fuel the rise of post-fascist parties, like Alternative for Germany (AfD) in Germany, that benefit massively from the problems of inflation and a major rise in energy prices as a result of the Ukraine war. Emissions correlate with the rising tide of right-wing extremism, and the threat of AI-based automation and job loss plays into its hands too.

The best answer for why the characters in ‘The Hardest Part’ are running, therefore, is perhaps not even that they are so keen to win the race. Instead, perhaps they are running away from accepting that, though the race and its consequences are real, it remains unclear whether it bears social benefits for anyone other than major corporations, authoritarian states, emerging war economies, fossil and nuclear energy providers, extreme right-wing organisations and the like. The urge to avoid this realisation by running away as quickly as possible is, of course, perfectly understandable; after all, to expose oneself to accepting that this is the main outcome of this race is probably the hardest part.

Moreover, the reason for all this activity, rush and competition is far from clear. AI is being touted as the goal, but what exactly was the question, again? To be sure, big data analysis and statistical pattern recognition could lead to massive breakthroughs in many areas of science, such as cancer treatments, but this is not being prioritised right now. There is no significant financial incentive to do so – let alone a new Manhattan project. Machine learning technology could even potentially be used to develop a more rational way to distribute resources, optimise energy consumption, lessen massive inequality, improve health and social systems, and prevent wasteful misallocation, but this would probably be labelled communism, not AI. For now, the most likely use case for the output of all these petrol-guzzling mega-data centres is instead given by Apple, who state that their Apple Intelligence system will be able to identify and prevent overlaps between a work schedule and a kid’s birthday party.

Is this good enough reason to fall back on fracking and other fossil energy sources to supply enough energy to sustain a race towards an AI monopoly? Or is it just a sign that nobody actually knows where the race is headed (besides making weapons) – or, most important of all, why? If you look closely, the actors in Paul Trillo’s video are also running all over the place; they turn left and right, without any clear direction. The colorful renderings seem like a substitute for the purpose of generative AI that has so far been missing. In the absence of a killer app, the industry tries to fall back on the original killer apps – weapons.

The race for AI continues nonetheless. Its participants ignore the environmental and social collateral damage, perhaps in hopes that they will somehow discover a goal during the race without causing too much damage. The one thing that’s certain is that they will keep running.

2 Burnt-Out Images: A Thermodynamical Turn

When someone believes they are some kind of messiah, they are said to have fallen victim to Jerusalem syndrome. In tech, however, self-declared messiahs display a Prometheus syndrome – after the mythical figure who stole the fire from gods.

One example is Emad Mostaque, the former CEO of AI image generator Stable Diffusion, who describes his tool in precisely such terms: as ‘bringing fire from the gods of creativity to the world’.¹ But Mostaque is not the only tech executive to invoke the fire-poaching hero. Google CEO Sundar Pichai, too, has directly compared artificial intelligence to fire: ‘AI is one of the most important things humanity is working on. It is more profound than, I dunno, electricity or fire,’ said Pichai at a town hall event in San Francisco in January 2018.² Similarly, Sam Altman, CEO of OpenAI, has stated that AI is the most important new technology since the taming of fire and the wheel.³ And, in 2022, Microsoft released a fork of OpenAI’s GPT model called ‘Prometheus’, integrated into their search engine Bing and parts of their software. The use of Promethean vocabulary by this crowd is not surprising, as this mythological figure was one of Ayn Rand’s favourite mythological figures, celebrating individual entrepreneurialism.

Fire thus becomes a symbol for so-called AI technologies and, in extension, digital-image generation based on it.

Fire as Technology

The relationship between fire and technology is ancient. Likely the most important tool of humankind, fire may even have altered the human body and brain: cooking food probably brought modern humans into being by modifying their brains and digestive tract. Fire may also have contributed to the rise of language, the division of labour and development of social forms.

This technology was initially associated with what came to be known, much later, as domestic labour: as hearth, candle, spark, combustion and electrical current. It helped humans to survive in colder environments and thus to migrate into different regions. It assisted in the shaping of landscape and the creation of paths by burning down vegetation or making the interiors of caves accessible, and domesticating space to some extent. It made night-time available for human activities. It made the creation of paintings in dark and remote cave passages possible. As such, it has dominated human attempts to control their surroundings.

Philosopher Bernard Stiegler describes the way humans started to modify their environment via steam engines and combustion motors using equally Promethean metaphors: ‘The Anthropocene epoch can appear … only starting from the moment when the question of the cosmos is itself grasped as that of combustion, in both astrophysics and thermodynamics – but, therefore, also in relation to this exceptional pharmakon that is domesticated fire, fire as that artifice par excellence delivered to mortals by Prometheus.’⁴

Medium Hot and Cool

When it comes to AI information processing, the invocation of fire is not only metaphorical; it is also one of its conceptual frameworks. Emad Mostaque’s flagship image generator is called Stable Diffusion for a reason: it is a so-called diffusion probabilistic model,⁵ which has its origins in the heat equation developed by nineteenth-century physicist Joseph Fourier.⁶ This formula describes how heat spreads through a medium, a dispersal which becomes an important template for some parts of machine learning–based image-making. In this thermodynamical paradigm, a detailed, intelligible image is seen as being ‘cool’, then is diffused into ‘hotter’, less organised articulations, and ultimately into random noise. Image generation proceeds by reversing this process of entropy and recovering, or ‘restoring’, an image from noise.

The move towards a thermodynamical paradigm, however, has many unexpected consequences. Its stochastic turn orients images away from indexical representation, towards prediction and approximation via averaged renderings based on giant datasets. The thermodynamical paradigm links physics with information theory and increasingly encroaches on image generation. In information theory, as media scholar Nicole Starosielski observes, ‘the idea that signal exchange is equivalent to heat exchange can be traced back directly to Claude Shannon’s theory of information, which advances thermodynamics-inspired concepts such as entropy in the description of communications systems’.⁷ Other areas inspired by thermodynamical paradigms include statistics, finance and climate science.

This new environment for image production presents a decisive break with a photographical or, more generally, an optical paradigm of image production based on causality that dominated much of twentieth-century thinking about media. In principle, within traditional optics, the route of every photon could be ray-traced back to its origin. In a thermodynamical environment, however, causality is replaced by probability, and ray-casting by forecasting.

The optical paradigm was associated (partly via photography and cinema) with Newtonian physics, the laws of reflection and refraction, shooting (in all senses of the word), factory production and the military (by way of camera technologies relating to film transport and emulsion production). In this context, a ray of light can be stopped or interfered with during transmission, thus leading to an understanding of noise as disruption or scrambling of signal.

Burnt-Out Images

In a thermodynamical environment, however, noise becomes an indication of increasing disorder – but also a raw material for image production.

Diffusion probabilistic models (formulated as ‘Markov chains’) first add random noise to the available training data in a so-called forward diffusion process, then reverse the process to recover the data (fig 2.1).⁸ An image is drowned in noise, then the noise is removed and the process remembered. Next, the removal process is applied to random noise samples, thus seemingly reversing entropy and disorder.

As one researcher documents:

Diffusion processes are continuous-time, continuous-state processes whose sample paths are everywhere continuous but nowhere differentiable. They arise in many natural phenomena and in some sense are generalized versions of the Brownian motion.

… In the fields where diffusion has been applied, it has been used to model phenomena evolving randomly and continuously in time under certain conditions, for example, security price fluctuations in a perfect market, variations of population growth in ideal conditions, and communication systems with noise.⁹

Figure 2.1. A graphic rendition of the diffusion denoising process (Jonathan Ho, Ajay Jain, and Pieter Abbeel, ‘Denoising Diffusion Probabilistic Models’, arXiv, 16 December 2020).

Figure 2.2. Exchanging noise with a JPEG of a leopard seems to work, too (Tom Goldstein [@tomgoldsteincs], ‘Diffusion models like #DALLE and #StableDiffusion are state of the art’, X, 24 August 2022, 8:15 p.m.)

At the same time, it remains patently unclear how, exactly, diffusion models work and whether random noise is really a key factor in AI-image generation. One researcher exchanged Brownian noise with a picture of a leopard – and still managed to generate plausible imagery (fig 2.2).¹⁰

In the absence of precise explanations, it is, however, notable that most scientists definitely rely on thermodynamical terminology to describe what image-generation models are supposedly doing – perhaps simply to align their rhetoric with adjacent fields such as finance and information theory.

From the Camera Obscura to the Camera Casino

Image generators are not the only operation that use heat diffusion, in part, to describe their operation. The pricing of stock options is predicted using a similar body of mathematics.

Fourier’s heat equation is a so-called partial differential equation (PDE), which uses random variables to model unknown changes over time. A very famous PDE is the ‘Black-Scholes equation’, first published in the 1970s, which promises to forecast future option prices.¹¹ Noise – used as a stand-in variable for random processes – plays a central role in seemingly domesticating chance and making it stochastically predictable.

This promise spawned a huge industry within just a few years. The derivatives market exploded and, with it, the invention of ever more derivative financial products. Debt became an asset. By 2007, annual derivatives trading in the international financial system grew from US$100 trillion to $1 quadrillion.¹²

In time, many bankers and traders forgot that they were relying on a form of wishful thinking; they used the equation as a kind of talisman, a ‘bit of mathematical magic’. It worked because everybody believed in it; in reality, however, it failed to predict the actual likeliness of extreme adverse events in a situation already destabilised by aggressive risk management. With the banking crisis of 2008–09, a house of cards of financial alchemy and magical thinking collapsed.

It is unknown if Black-Scholes would have produced the same outcome using the JPEG of a leopard as a random variable instead of noise; perhaps this would even have increased its magical aura as a key tool of predatory capitalism.

Derivative Images

Sociologist Donald MacKenzie, in his seminal investigation of Black-Scholes entitled An Engine, Not a Camera, makes the case that such PDEs are not analytical renditions of any situation but, rather, active participants in co-creating it. He refers to the formula as an engine of inquiry, a motor. A similar motor is now embedded into machine learning–based image generators – an engine as a camera, so to speak.¹³

Prior to the meteoric rise of generative AI, media scholar Jonathan Beller defined derivative images as programmable images in the service of capital, transforming every social quality into quantity within racialised, extractive industries.¹⁴

Additionally, the derivative image is now also literally derivative: it is derived from billions of other images in the training data. According to copyright law, ‘derivative’ means work that includes elements from a copyrighted work. The underlying image may be a derivative of an original, or even a derivative of a derivative. Practically speaking, creating these derivatives works to avoid copyright issues and enable the large-scale theft of data.

The crash of this system of value in relation to images is already happening. This has led to a widespread disbelief in any truth value of any image or information – whether AI-generated or not – with wide-ranging consequences for public discourse (or whatever remains of it).

Making Disorder Profitable

In this kind of value system, risk and disorder themselves become assets. New financial derivatives try to put a price on climate risk in order to transform it into a set of opportunities – aiming to appraise the value of climate change itself and turn it into a speculative market. This mechanism relies on the idea of markets acting as superior information processors that ‘sense’ reality via pricing. As the philosopher of technology William R. Morgan observes:

For ESG [environmental, social and governance] finance, the climate catastrophe is not, as a matter of necessity, something to be avoided. It is rather a source of expected risk, the existence of which demands the invention of products whose role it is to transfer risk from those who do not want exposure to it to those that do. This is the power of ESG finance, not to blunt the risk of disaster, but to facilitate the pricing of catastrophic risk so as to enable its being bought and sold.

Through these mechanisms, the future heat or climate chaos itself may become a market full of opportunities for profit, but also a system of sensors to capture reality via pricing. As Morgan writes, ‘Derivatives such as these appear to offer absolute certainty in reply to seemingly absurd questions. What is the price of a hurricane in sovereign debt markets? What about several hurricanes? … What is the cost of exposure to climate risk?’¹⁵

Let us add to this series of questions: What is the price of a riot or rebellion? What is the price of entropy? How does it become a valuable source of disorder to be gambled on or exploited as raw material?

In terms of image-making, markets are becoming ‘cameras’ – or, more generally, data processors – environmental sensors that render reality as a pricing system.

Stochastic Point Clouds

Thus, image generation moves even further away from any indexical contact with reality, from observation and exposure to actual, unaveraged situations. To be clear, image generation does not have anything to do with reality per se. Images can be sheer fictions, dreams or hallucinations – basically anything at all. Nevertheless, reality and observation, and the numerous questions arising from such notions, were important factors for consideration within an optical/indexical paradigm of representation. After all, fidelity to observable reality was based on ray-tracing the trajectories of actual photons / light rays back to their origin. This part is now filtered through statistical procedures.

In a stochastic paradigm, contact with any external reality is heavily mediated through the averaging of large-scale datasets. This statistical realism is based not on observation but on quantity – on the idea that scale, in itself, will capture a statistically relevant sample: if one just captures ‘everything’, a real underlying pattern will end up being captured too. Such thinking not only confuses the internet with reality but also privileges quantity over actual observation and analysis. Most importantly, the unexpected and contingent cannot enter these calculations by default.

In contrast to this, the paradigm of documentary photographic practices allowed for the possibility of unexpected things to happen in front of the camera, to expand the realm of what was supposed to be visible and knowable – to collect new primary-source data, so to speak.

The image matter of stochastic renderings are huge data clouds, as well as quasi-astrological attempts to interpret and identify inherent features and patterns. One underlying assumption is that it is both unnecessary and infeasible to record individual events, and that their statistic relevance is minor. But this also erases the possibility of ever capturing something which is not yet already known and included into the dataset. The dataset becomes something like a VR headset that replaces actual vision with heavily redacted data visualisations.

Machine Dementia

The process of conjuring up images from granulated data could easily turn into a feedback loop. People generate data, which is then used to train AI models. In a next step, AI models generate more data. Then, these synthetic data are also used as training data to train more AI models. This, in turn, creates so-called model collapse, which one computer scientist refers to as ‘a degenerative learning process where models start forgetting improbable events over time as the model becomes poisoned with its own projection of reality’.¹⁶

When undergoing model collapse, machines start to stammer and repeat elements from the range of most likely predictions, which sound rather demented to human beings (table 2.1). Total probability – the ultimate norm – is a kind of machine dementia.

Conditioned to be as ‘normal’ as possible, the model thus starts sounding psychotic. As journalist Alfonso Maruccia puts it, ‘Like a JPEG image recompressed too many times, the internet of the AI-driven future is seemingly destined to turn into a giant pile of worthless digital white noise.’¹⁷

The problem is not just the production of noise and nonsense; it is an overall acceleration of entropy, in the sense of breaking down meaning and matter, and of warming the planet: ‘Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide’, Maruccia writes, ‘so we’re about to fill the Internet with blah.’¹⁸

e/acc

Some tech ideologists appreciate accelerating entropy. They advocate the development of artificial general intelligence (AGI) to increase disorder by consuming an enormous amount of energy. Why would this make any sense? Because, according to Guillaume Verdon – a physicist, self-declared thermodynamical priest and evangelist of ‘effective accelerationism’ (often abbreviated ‘e/acc’) who tweets under the moniker of @BasedBeffJezos – the second law of thermodynamics proves that the universe itself aims to increase entropy. This is apparently the ‘will of the universe’, and ‘the universe exponentially favors (in terms of probability of existence/occurrence) futures where matter has adapted itself to capture more free energy and convert it to more entropy’.¹⁹

Table 2.1. Model collapse

Example of text outputs of an OPT-125m model affected by Model Collapse – models degrade over generations, where each new generation is trained on data produced by the previous generation.

Input:

some started before 1360 – was typically accomplished by a master mason and a small team of itinerant masons, supplemented by local parish labourers, according to Poyntz Wright. But other authors reject this model, suggesting instead that leading architects designed the parish church towers based on early examples of Perpendicular

Outputs:

Gen 0:

Revival architecture such as St. John’s Cathedral in London. The earliest surviving example of Perpendicular Revival Architecture is found in the 18th @-@ century Church of Our Lady of Guernsey, which dates from the late 19th century. There are two types of perpendicular churches: those

Gen 1:

architecture such as St. Peter’s Basilica in Rome or St. Peter’s Basilica in Buenos Aires. There is no evidence that any of these buildings were built during the reign of Pope Innocent III, but it is possible that they may have been built during the reign of his successor, Pope Innocent

Gen 7:

architecture in England. In an interview with The New York Times, Wright said: “I don’t think there is anything wrong with me being able to do what I want to do. It just doesn’t work for me. He added: “I don’t know if you can call it funny,

Gen 9:

architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @-

Source: Ilia Shumailov, ‘The Curse of Recursion: Training on Generated Data Makes Models Forget’, arXiv, 14 April 2024.

Verdon expands on a very interesting, but still speculative theory by scientist Jeremy England, who claims that life arose as a form of spontaneous self-organisation of matter in relation to external energy sources. According to England, some systems are able to dissipate more energy than others, which gives them an evolutionary advantage.²⁰ While this theory remains to be validated and is restricted to the molecular level, Verdon applies it to capitalism as an intelligent ‘meta-organism’. Because capital is able to consume/diffuse a lot of energy, he muses, it must be a higher form of being.

Verdon calls for humanity to ‘climb the Kardashev scale’ to convert even more free energy into unavailable energy and bring the universe closer to heat death.²¹ The Kardashev scale is a ranking of civilisations according to how much energy they are able to use. A type 1 civilisation uses all the energy on its planet; a type 2 civilisation harnesses all the energy produced by its nearest star; and a type 3 civilisation captures all the energy of its galaxy. The more energy a civilisation captures and uses, the more in line it appears with the presumed ‘will of the universe’. Thus, according to Verdon, the development of technology should be accelerated to ascend the Kardashev scale by building AGI.²²

According to the e/acc ideology, AGI’s main purpose is not to solve computational or scientific problems but to burn through tons of energy, thus increasing entropy and accelerating heat death. Burning becomes an end in itself. This, however, comes at a massive cost, requiring massive energy resources and an industrial-scale infrastructure that consumes power and increases overall disorder, including social disruption.

As Bernard Stiegler notes in his observations on the Anthropocene, ‘The thermodynamic machine, which raises, in physics, the new and specific problem of the dissipation of energy, is also an industrial technical object that fundamentally disrupts social organizations.’²³ The thermodynamical imaging infrastructure is disruptive in that it consists of a massive system of burning, selling and profiting from fossil fuels, wasting resources, heating the atmosphere and, as a consequence, causing the devastation of the environmental and social fabric. Some cultists even advocate accelerating this process – probably after placing some financial bets on it. Where there is the fire of combustion and heat, smoke, ash and embers are not far. So are burnout and an acceleration of disordering, entropic processes. AI image generation is thus embedded in what one could call a larger digital political economy of entropy.

In her influential book Media Hot and Cold, Nicole Starosielski describes parts of this system:

The most powerful media organizations of the twenty-first century will be thermal. The circulation of images, sounds, videos, and texts will depend on a massive regime of heating and cooling. Data and networks, like the people they connect, will be ever more fragile. Too hot or too cold, and the platforms will collapse. Digital infrastructures – data centers, network exchanges, and fiberoptic cables – will drain the planet’s energy in order to create a stable thermal environment – not for people but for information.²⁴

Political Economy of Entropy

In May 2021, the Colonial Pipeline, an American oil pipeline system, suffered a ransomware cyberattack. Its operator, the Colonial Pipeline Company, stopped all operations to contain the attack. Within several hours, the company paid seventy-five Bitcoin, then worth $4.4 million.²⁵ After the ransom was delivered, hackers provided an IT tool to the company to restore the system. However, the attack had already brought about serious side effects. Airlines and airports were disrupted for lack of fuel. Panic-buying of gas caused a bump in prices and fuel shortages. Some people even filled plastic bags with gasoline.²⁶

A few weeks prior, a new malware, called Prometheus, had been launched. Within a short time, it would infect thirty organisations, including banks, governments in North America and Asia, and numerous companies such as Colonial Pipeline. A Prometheus attack basically takes a server hostage by encrypting all the files on it and demanding a ransom (which they call a ‘timed ticket’) for people to access their own data. If the victims (called ‘customers’) don’t pay, their data are put up for auction. Payment was demanded in the cryptocurrency Monero, which was considered ‘secure’, or Bitcoin, at a higher cost due to its traceability. By doing so, the kidnappers effectively separated people from their data and demanded money from them to access their own files. A Prometheus attack thus combines several elements: theft of knowledge, seizure of infrastructure and establishment of a kind of monopoly access to it. If people want to access it, they have to pay.

One can see elements of such hostage-taking within digital industries as well. It seems likely that the Promethean projects advertised by Mostaque, Altman et al. in fact amount to a legal version of a Prometheus attack. After all, many data corporations control access to people’s data. The producers of data have to pay to access their own data/projects through proprietary clouds and gateways. The difference is that such extortion is entirely legal, since those corporations own the infrastructure on which their customers/hostages rely. Contemporary Prometheus impersonators thus not only steal knowledge/fire but rent it back to humans (instead of simply handing it out as a gift). They train workers to become dependent on proprietary pipelines that ultimately heat atmospheres and destabilise environments.

Prometheus Gets Fired

In late March 2024, Stability AI founder Emad Mostaque took the stage for a PR announcement. An AI-generated Aristotle announced him as ‘a modern Prometheus’ in faux-Greek jargon: ‘Under his stewardship, AI becomes the Herculean force poised to vanquish the twin serpents of illness and ailment and extend the olive branch of longevity.’

‘I think that’s the best intro I’ve ever had,’ Mostaque said.²⁷

But simultaneously, Stability AI, the company that had made diffusion-based image generators famous, was in deep trouble. Its key researchers – the team that had initially created Stable Diffusion at two German universities – had resigned. Creditors were putting on pressure. The disclosure of abusive child pornographic imagery within the training data used for Stable Diffusion had barely been addressed. Stability’s position on stealing copyrighted material was criticised by former personnel. Meanwhile, three copyright infringement lawsuits brought by Getty Images and a group of artists in the US and UK claimed Stability AI illegally used their art and photography to train the AI models powering Stable Diffusion.

The main reason for Stability AI’s crisis, though, was acute financial instability. Stability AI was burning through money – in the form of compute, or the operation of graphics processing units (GPUs) chained together on Amazon’s Web Services cloud. As one journalist reported, ‘The costs associated with so much compute were now threatening to sink the company. According to an internal October financial forecast seen by Forbes, Stability was on track to spend $99 million on compute in 2023.’

The result? According to Mostaque, a new version of the Stable Diffusion image generator could now generate ‘200 cats with hats per second’.

In the end, Stable Diffusion would be described as ‘a giant vacuum that absorbed everything: money, compute, people’.²⁸ In late March 2024, Mostaque resigned.

3 Medium Hot: The Political Economy of Entropy

‘It was ruthlessly effective. It dispersed crowds and could turn a “protest into a screaming mob”.’

– Jen Kirby, ‘The Disturbing History of How Tear Gas Became the Weapon of Choice against Protestors’¹

In the past decade, tear gas clouds drifted though cities from Tahrir Square to Hong Kong, dispersing crowds into gasping individuals. Pictures and sounds spread too, via cloud platforms and social media, triggering more protests. They seemed to ignite more action wherever they circulated, often creating intensity and contagion. Waves of heated activity rippled through territories and often led to a new jumble of competing images of violence and oppression.

However, there is now an additional function of protest imagery: as training material for so-called AI prompt generators to create images and videos. Repositories like LAION-5B become incidental archives of protest imagery, forming large databases in which text/image pairs are collected and upon which so-called generative models like Stable Diffusion are trained. Thus, protest imagery becomes raw material for the generation of new images.

What does an average protest look like? I asked a generator to render an image using just the word ‘protest’ on standard settings. The result is seen in figure 3.1.

Figure 3.1. A Stable Diffusion 1.5 rendering for the prompt ‘protest’.

Instead of protesters, it generated a figure which seems to be a policeperson looking at some blurred group of figures and a big screen in the background. Is there tear gas dissolving to blur the view in the back? Or is this just a visual short cut to minimise render time and detail? Why a policeperson, too? Is it even a policeperson? Is it a military uniform? Militia? What about the mirrored police clone, cut off on the right edge of frame?

There is a huge difference to iconic representations of protest such as Haskell Wexler’s film Medium Cool. Wexler’s famous film was partly shot amid the riots at the 1968 Democratic National Convention in Chicago. It strongly foregrounds the act of image-making itself. The main protagonist is a cynical TV cameraperson, trying to capture as many violent or attention-grabbing scenes as possible. Medium Cool focuses on the infrastructure of imagery, its market and the subjectivities created by it.

The title of the film is a loose reference to philosopher Marshall McLuhan’s idea of television as a ‘cool’ medium. ‘Cool’ media are ‘low definition’ because they are more compressed and demand more ‘completion’ by viewers. In contrast, McLuhan claims that ‘hot’ media – like print, photographs, radio and movies – are rich in sensory data and therefore more immersive.

In the past decade, both presumedly ‘hot’ and ‘cool’ media transformed into more infectious and mobile phenomena. While still affective and mostly short lived, they could be low-res or as highly compressed as memes. It was precisely this compression that facilitated their circulation and potentially their effects.

If We Burn

In the years since the Arab Spring in 2010, the effects of the dissemination of protest images via social media have been hotly debated. Social media corporations quickly seized the opportunity to present themselves as catalysts for the series of uprisings and revolts in many countries in the 2010s. The legacy of this moment is, however, ambiguous.

In his book If We Burn, foreign correspondent Vincent Bevins analyses the decade of mass protests in Brazil, Turkey, Hong Kong, Egypt, Syria and Tunisia from 2010 onwards and concludes that many of the situations ended up even more authoritarian.² Protests did spread rapidly, for example, following the self-immolation of cart vendor Mohamed Bouazizi in Tunisia in late 2010. In many places, however, a massive backlash followed, if not several – like the consolidation of Recep Tayyip Erdoğan’s religio-authoritarian regime after the 2013 Turkish Gezi Park protests and the subsequent campaigns against dissenters and open warfare against Kurdish minorities.

According to Bevins, many factors played a role for the ambiguous outcome of the 2010 protest decade. One was the refusal of movements to claim political representation, to create platforms or to come up with shared demands. In many cases, there was no plan beyond making a revolt happen. From then on, things were supposed to evolve by themselves. They usually did – though not as expected. This form of loose, populist revolt was quickly adopted by reactionary forces, who were, ultimately, more successful.

Intellectual historian Anton Jäger describes this agitated situation as ‘a form of politicization without clear political consequences’ that he calls ‘hyperpolitics’:

Post-politics was finished off by the 2010s. The public sphere has been repoliticized and re-enchanted, but on terms which are more individualistic and short-termist, evoking the fluidity and ephemerality of the online world. This is an abidingly ‘low’ form of politics – low-cost, low-entry, low-duration, and all too often, low-value. It is distinct both from the post-politics of the 1990s, in which public and private were radically separated, and from the traditional mass politics of the twentieth century. What we are left with is a grin without a cat: a politics without policy influence or institutional ties.³

Additionally, the rejection by so-called horizontalists of engagement with political representation had a paradoxical consequence: it was supplemented by a major push to increase the symbolic representation of marginalised groups within media and culture. The more political representation was rejected, the more importance was accorded to cultural representation, contributing to a jumble of (often competing) claims for attention and recognition and to a marginalisation of solidarity in favour of identity. The focus shifted to language and imagery, or, in classical terms, the superstructure; the base of its production was taken for granted.

Digital and, in particular, social media have, rather, contributed to stabilising and helping authoritarian regimes and tendencies during this period – not the other way around. They have also massively contributed to events like the military persecution of Rohingya in Myanmar starting in 2016, or the storm of the US Capitol on 6 January 2021. Their effect on progressive movements has been just as ambivalent. More than a decade of social media–fuelled protests have led to a series of volatile, unsynchronised, high-intensity outbreaks with widely divergent goals and mixed outcomes. ‘While the internet has radically lowered the costs of political expression’, Jäger notes, ‘it has also pulverized the terrain of radical politics, blurring the borders between party and society and spawning a chaos of online actors.’⁴

Seen from the vantage of digital infrastructure and production, though, the jumble of worldwide protests looks strikingly simple. Until now, the vast bulk of digital communications by protest movements (and beyond) remains staunchly administered by a few corporate platforms within different geopolitical blocs.

In the decade since the Arab Spring, this has first led to the creation of echo chambers and polarisation on platforms like Facebook, then smaller gated communities in places like Discord and Telegram creating fragmented, increasingly insular affinity groups, sheltering in the so-called dark forest. In the era of TikTok, this atomisation is pushed even further, or maybe full circle. TikTok is ushering in an updated version of television, McLuhan’s ‘cool medium’, with its top-down, one-way style of communication and isolating mode of address. Outside of the Western media sphere, various other digital monopolies create their own sealed off areas of influence.

Not only ‘hot’ media have changed since McLuhan’s confusing (and quite random) classification, but also ‘cool’ media. Moreover, with the advent of statistical image generators, entropy enters image production in a very direct way. Today, one can rephrase the question of hotness or coolness in a straightforward way: What is the temperature of rendering generated protest images?

In many so-called AI-models, there is a precise number for this value, indicating the amount of output randomness. What kind of heat do these models release into which system? Machine learning (ML) images transform ‘hot’, disordered noise particles into ‘cool’, more orderly renderings. They use the disorder of noise as a raw material, ‘cooling’ it in a process reminiscent of air conditioning, and releasing more entropy into other parts of the system as a by-product. Medium cool transforms into cooled media.⁵ The more detail and immersiveness, the more compute is necessary, and the more literal heat is released into the atmosphere. Image generation thus enters the realm of thermodynamics, of information, finance and entropy, of heat and energy, of warming climates and non-linear weather effects.

Where, then, one may ask, is the equivalent of Haskell Wexler’s cameraperson, calmly recording the mayhem within clouds of tear gas? Where are the image workers of the contemporary moment, who would bring to the fore the base of image creation and the labour and resources involved? Well, they are present – but in an unexpected way.

Absence

Let’s return to our quite randomly generated render from the beginning. You might think all protesters have been expunged from this scene. Only helmeted policepeople are left (fig. 3.1).

But what is actually happening on the screen that the uniformed person is watching?

What is this white shadow, this negative image? It is an actor who is in fact missing from the image, who has left the image. This person might have walked out of the screen. The screen is on strike, so to speak.

In 2023, writers and actors organised in US unions and guilds went on strike for many months. Points of contestation included the use of so-called AI in film production, scriptwriting, face-printing of actors, voice cloning and so on. These were not the first protests against AI industries and their labour conditions. The ‘African Content Moderators Union’, for one, was founded in April 2023 by clickworkers who had worked for AI tools used by Meta and ByteDance, among others. This process was catalysed by the case of Daniel Motaung, who was fired from his job as a content moderator after trying to unionise co-workers at an outsourcing company called Sama, which became famous for underpaying and traumatising their employees.⁶

One of the most remarkable things about ML image generators is not their output but that they have already spawned a whole range of protests. One could even call these recursive protests, as they target the infrastructure of digital image production. And this is where ML-based image-making had its strongest impact until now: in catalyzing actual protest and organised counteraction on many levels – importantly, not just in the streets. No recent communication technology has spawned so many protest activities. In fact, the recent history of these apps can be written succinctly through the different protests against them. It seems that Haskell Wexler’s disaffected cameraperson may have finally joined the protest, but only after being fired, and subsidising his own labour for many years, soon to be fired again.

Protests against AI Image Generation

Let’s start with a quite well-known and early protest against ML-based image generators.

On 5 December 2022, Bulgarian artist Alexander Nanitchkov tweeted: ‘Current AI “art” is created on the backs of hundreds of thousands of artists and photographers who made billions of images and spend time, love and dedication to have their work soullessly stolen and used by selfish people for profit without the slightest concept of ethics.’⁷

Nanitchkov also posted a logo featuring the letters ‘AI’ crossed out in red. Members of the ArtStation community started reposting this composition, dominating the online platform’s trending page. ArtStation hit back by taking down the anti-AI logos, claiming they violated their terms of service. As one journalist reported, ‘Protestors were concerned that AI-generated art is derivative of the labor of human artists and often uses their work without attribution or compensation’ (fig. 3.2).⁸

This action started a first wave of protest against the expropriation of individual intellectual property (IP) by AI corporations. In a second wave, individuals and companies started suing AI corporations. In 2023, at least ten lawsuits were launched against Microsoft, OpenAI, Anthropic, Stability AI and other companies to stop the looting of data to train large language models, alleging ‘systematic theft on a mass scale’. According to the Week, ‘Over 9,000 authors signed an open letter from the US based Author’s Guild that called out the “inherent injustice” in generative-AI companies “exploiting” their work without consent or compensation. The authors include Margaret Atwood, James Patterson and Jodi Picoult.’⁹

Figure 3.2. Screenshot of ArtStation trending page, detail.

This Cambrian explosion of opposition to AI-related data-harvesting directly addresses the most important fallacies (or clichés) about technological progress, namely, that technological progress is (a) unavoidable, (b) automatic and (c) will benefit everyone. However, progress is not automatically created by this or that technology, let alone for everyone.

As economists Daron Acemoglu and Simon Johnson have shown, social progress is distinct from technological progress, and there is no causal link between both. In their recent book Power and Progress, they state that the benefits of productivity gains through technology were never shared voluntarily by their so-called owners, or, more correctly, their privatisers.¹⁰ To put it briefly, social progress was created by social movements, not by inventors or tech companies. A combination of regulation, taxation, bottom-up organisation (such as through unions), plus a civil society drive to rewrite the narrative of automatic progress had to come together, over and over again, to achieve social progress. And throughout history, people had to reclaim their share of technological benefits from technological monopolies. Without this active fight, they would never have gained a share of the benefits of technology and other cooperative endeavours.

Models for Redistribution

There are many ways in which the redistribution issue could be solved, and many suggestions for how to achieve this. Let’s start with the most complicated one: the cooperative model.

Put very simply, this model proposes to redefine privatised data loot as cooperatives. Both scraped data collections and the models trained on them would be redesignated as data commons, collectively governed through democratic participation and acknowledging the collaboration of all the actors involved. Such models already exist: my old-school German artists’ rights society is pursuing a model whereby they claim royalties on Xerox copies and social media circulation of materials. They argue that they are, by default, representing artists rights, regardless whether artists have individually agreed to this or not. Proceeds are redistributed among the members of the artists’ rights society. Other organisational models include ‘data unions’, as advocated by computer scientist Jaron Lanier, and prototypes developed by economist Francesca Bria, among others.¹¹

Then again – bearing in mind that digital royalty distribution models didn’t work well in the past and require a lot of additional bureaucracy – perhaps there is a much-easier method to bring about social progress.

Taxation has been a tried and tested technology since the end of the Neolithic. It’s only fair for society to reclaim what ML companies cost them in terms of providing a free sandbox for unpaid testing, free data provision and large-scale social experimentation. Regulation is another possibility.

Hopefully, this era of protests is a step forward from more than a decade of social media–driven protests, in which image circulation of protest images created a kind of diffusion heat map of political affect, waves of demonstrations, uprisings and heated political white noise, but not so much in terms of sustainable infrastructures to organise structural change. One way to change this is to make protest recursive, too, by directing it against the tools of image generation and diffusion – its financialised imaginaries, its modes of expropriation and exploitation, and the whole empire of corporate extraction of people’s data and labour for subprime repackaging.

Recursive Protest

Recursive protest is not only fascinating as a general political phenomena. If analysed from within the thermodynamical framework, it reveals another aspect: namely, that the protests and strikes are not just disruptions of ordinary routines and procedures but effect the disruption of disruption. They are an attempt to create order by way of organisation, an active act of what philosopher Bernard Stiegler calls negentropy.¹² For people treated like isolated suspended particles, violently tossed about by some kind of financial, ecological and social tsunami, denoising is an act of organisation, of partially reversing an entropy, which is not just given, but actively produced to exploit its instability effects. Increasingly, workers in digital industries take on the role of noise particles, being burnt out, dispersed and moving about randomly, making them vulnerable to exploitation.

In this case, reverse entropy, or negentropy, would include workers organising and rebuilding their energy and social compositions. It would form the material counterpart to conjuring up images from random noise. Whole societies could be restored from political fragmentation, which has pushed them towards a profitable form of entropy. Organisational negentropy expresses itself by creating coordination between entities atomised by targeted identification, micro-labelling and desolidarisation.

One of the most important consequences of recursive protests is to rebuild links and coordination – not only to share technological benefits (if there are any) but to disrupt disruption, as it were; to undo isolation and exposure to artificial technological anxiety and exploitation.

This process may not be captured by the statistical renderings of protest. Its protagonist – the one on strike – appears in absence and negation: as a hole in the fabric of entropic engineering.

4 Knuckleporn or Phocomelia: GenAI’s Dialectics of Enlightenment

The use of generative AI tools to portray humans has posed persistent problems. Human bodies are complicated. Fingers, movements and faces have turned out to be difficult subjects for image generators, often producing hands with anywhere between four and seven fingers and a lot of superfluous joints in between. This issue is not trivial. The question is: How do those systems see humans? What does a human body look like when generated from statistics? What other kind of forces act on this type of representation?

However, these problems are slowly being solved and are about to go extinct, especially as the industry currently seems to be moving away from photorealistic depictions towards an aesthetic of fantasy-oriented illustration. The era when limbs ended up all twisted seems to be over. Well, almost.

There is an interesting counter-example to the general trend. And it serves as a prism for the investigation of some larger question of human bodies’ representation by AI image generators – precisely because they are no longer caused by strictly technical issues. Instead, they are caused by social and even moral double binds, or, to be more precise, questions of (missing) responsibility, liability and censorship. The immediate result of these problems is that generative AI’s output, in some cases, falls behind the pictorial realism of the Western so-called Renaissance into an age of almost-medieval monsters and cephalopods: generative AI’s own dialectics of enlightenment.

Figure 4.1. A rendering by Stable Diffusion 3, with the prompt ‘a girl lying in grass’.

Figure 4.2. A rendering by Stable Diffusion 3, with the prompt ‘a girl lying in grass’.

Consider the example in figures 4.1 and 4.2.

This creature emerged as an output to the quite harmless prompt ‘a girl lying in grass’, rendered by Stable Diffusion 3 (SD3) Medium – an open-source model released by Stability AI in June 2024. Researchers were able to uncover troves of dubious material, including child pornography in the training data of previous Stable Diffusion releases, leading to the temporary withdrawal of the whole dataset.¹ Stability AI thus seems to have overcorrected its training data base in order to exclude any NSFW material or straight-up porn.²

It turns out that a machine learning model needs to be trained on nudes – just like any human, by the way – to understand how human bodies usually hold together in the first place. To quote an expert: ‘How the weights affect each other with this stuff is that by not properly capturing penises means that fingers look funny now’ (figs 4.3, 4.4).³

Figure 4.3. A rendering by Stable Diffusion 3, with the prompt ‘a girl lying in grass’.

Figure 4.4. A rendering by Stable Diffusion 3, with the prompt ‘a girl lying in grass’.

Figure 4.5. Édouard Manet, Le Déjeuner sur l’herbe (The Luncheon on the Grass), 1863.

If one extends the prompt to the title of Édouard Manet’s famous painting The Luncheon on the Grass and specifies the number of people, one gets the kind of result seen in figures 4.6–10.

These samples show a healthy dose of gay porn influence, paired with a wide-ranging inability (or unwillingness) to deal with boobs, hands or feet, or any other kind of joint. They also reveal that SD3 has difficulties distinguishing between human bodies and meals; indeed, it seems to be an ideal base model for generating illustrated food menus for cannibals with a special knack for yummy joints. It is optimised to generate what one could call knuckleporn (figs 4.7, 4.8).

The Manet prompt results in chimeras, which have rarely been seen in Western image production since pre-Enlightenment ages; Dark Age creatures: a bestiary of basilisks, monopods and struthopodes. The regress in the ability to represent human anatomy recalls medieval depictions of monsters in unknown parts of the world lacking heads or endowed with a lot of bonus arms.

Figure 4.6. A rendering by Stable Diffusion 3, in the style of Édouard Manet.

Figure 4.7. A rendering by Stable Diffusion 3, in the style of Édouard Manet.

Figure 4.8. A rendering by Stable Diffusion 3, in the style of Édouard Manet.

Figure 4.9. A rendering by Stable Diffusion 3, in the style of Édouard Manet.

Technologist and journalist James Bridle’s diagnosis of an imminent ‘dark age’ full of digital superstitions gets a pictorial confirmation. He argues: ‘That which was intended to enlighten the world in practice darkens it.’⁴ By the presumably enlightening part, he means an onslaught of digital information. And the renderings prove it. These monsters are not issued from a malfunction of technology – those times are past – but from the pictorial rules and legal and moral norms imposed by/on digital industries. These rules are at once prude and sensationalist, creating a tension that potentially twists and mutilates the bodies, with too many and too few limbs at the same time. They are also primarily intended to avoid liability if someone were to create illegal porn using those tools. The startup knows very well that some user will ‘fine-tune’ the model (i.e., bring back the illicit content and start generating realistic porn). They just don’t want to be the ones responsible for it. Thus, knuckleporn is a result of an intentional refusal of responsibility.

Abstraction Cloaks

In 2013, a series of sculptures by Damien Hirst were unveiled in front of a hospital in Qatar dedicated to women’s and children’s health. The series of large-scale bronzes, called The Miraculous Journey, depicted the process of a fetus gestating in fourteen steps, from conception to birth. Shortly after being inaugurated, they were covered up by large canvasses, rendering the shapes abstract and quite intriguing. The reason for hiding them was never officially stated, but there had been a lot of criticism on social media about both the figurative character of the work and also the fact that the fetuses were, if that word makes any sense in this context, naked.

The covered shapes thus became expressions of the conflicts between art-lubricated tourism marketing and socially conservative values in Qatar, between art as spectacle and art as a (to some degree) faithful and unflinching depiction of the sometimes-stark realities of anatomy and pregnancy.

As the bronzes had, for a brief moment, been exposed, it was known how they looked underneath the cloaks. They were big, if not fat bronzes, some of which were slightly reminiscent of H. R. Giger’s version of aliens, portraying a reptiloid creature slowly evolving over the course of several statues to resemble a feisty human baby. Some of them looked pretty scary, exploiting maximum shock value. And yet, at this specific moment in time, there also was an unexpected vulnerability to these statues. Besides being feudal marketing gadgets, they also embodied an idea of anatomic fidelity – a medical realism, so to speak. If one subtracted, for a moment, the whole ploy of drawing eyeballs and generating tourist revenue for a socially very conservative Gulf petrostate repeatedly accused of sponsoring Islamist militias, one could also see Hirst’s statues as a defence of medical science and a rational, fact-based world-view. The monstrous creatures demand respect for the sheer anatomical facts of reproductive health issues, which were supposed to remain covered up and hidden from view because of social and religious conventions.

Hirst’s bronzes were officially unveiled five years later. Their meanings have surely transformed in the meantime. It is likely that my prior interpretation was relevant only to a specific moment, when the cloaking, paradoxically, highlighted some of the values to be defended in showing nudes in public (fig. 4.10).

Figure 4.10. A rendering by Stable Diffusion 3, in the style of Édouard Manet.

At its launch, SD3 might have profited from an automated cover-up function too. Just imagine the creatures in the image above all fitted with lovely abstract, sail-like covers. The generator could even have used the oversized tablecloth to wrap all four figures up into a single Christo-themed package. Just as the Berlin Reichstag looked much better when Christo and Jeanne-Claude wrapped it in 1995, Stable Diffusion’s creatures would have benefitted aesthetically from an abstraction cloak (which might also have hidden the disturbing puppeteering issue going on in the right-hand side of the rendering).

Let’s not forget: all these pictorial issues in SD3 will be fixed, or at least patched up, very soon by some anonymous users, who will have to assume responsibility on behalf of an unwilling company. The technical issues are transient, just like Hirst’s covered-up statues – snapshots of a quite short and specific moment in time. They hold no deeper meaning about the model or the technology itself; they are merely a reflection of the social constellations of all these different elements at a particular moment.

A Counteragent to Nerve Gas

There is, however, another way to interpret the missing limbs. Perhaps the model is not making mistakes at all but, in fact, depicts a specific kind of people: those with missing or non-normative limbs.

Maybe these renderings portray people affected by thalidomide poisoning. There are many real people with similar limbs, not by fate, or mistake, but because they were harmed by a medication marketed as safe and prescribed in the 1950s and early ’60s against morning sickness in pregnancy. Some were born with so-called phocomelia – a condition in which limbs resemble seal fins. SD3 could have developed the opinion that its subjects are affected by this condition. Why would this be?

The well-known AI researcher Yoshua Bengio points out that thalidomide today should be remembered as an example of the massive risk involved in trusting industries to act in the interest of the wider public.⁵ In a text which mainly deals with existential risk occurring through potentially rogue artificial general intelligences (AGIs), he mentions the thalidomide case as a potent warning of what happens when companies are blindly trusted to go forward with their product, whether AI-based or pharmaceutical. So, what was the thalidomide scandal about?

Thalidomide is a substance marketed in the 1950s by the West German pharmaceutical company Chemie Grünenthal. Sold (in Germany) under the name Contergan, it was used for treating conditions such as colds, flu, nausea and morning sickness in pregnancy. Taken in the early stages of pregnancy, it caused limb difference and a lot of other very serious neurological and internal damage to gestating fetuses. About 10,000 babies were born in the 1950s and ’60s with all kinds of congenital health issues. Many died, making the case of thalidomide the largest pharmaceutical scandal to date.

Chemie Grünenthal was established by Hermann Wirtz, a former Nazi Party member, after World War II. Wirtz hired a substantial number of former Nazi scientists with an astonishing combined criminal record. Otto Ambros, for one, had been part of the team that invented the nerve gas Sarin in 1939 at a military gas-production lab in Spandau. Sarin was created as a by-product of efforts to develop insecticides. He also worked on producing mustard gas, which was tested on inmates of a concentration camp. He was responsible for managing and building the Buna factory in Monowitz, part of an Auschwitz I. G. Farben plant, exclusively staffed by concentration camp slave labour. Primo Levi, a chemist who worked in this factory, famously described it in his writings.⁶ Ambros was sentenced to eight years in prison at the Nuremberg trials but was released after serving only three. He was the chairman of Grünenthal’s advisory committee during the development of thalidomide and a board member when Contergan was being sold.

Heinrich Mückter headed Grünenthal’s development programme. He had killed many prisoners in labour camps in Poland by experimenting on them with anti-typhus vaccines, without any legal consequences for him. Also on staff was Martin Staemmler, a medical doctor and one of the main proponents of the Nazi racial hygiene and eugenics programme as a coeditor of the magazine Volk und Rasse.⁷ He was classified as a ‘fellow traveller’ after the war.

Another former Nazi hired by Chemie Grünenthal was Heinz Baumkötter, who had been an SS officer and the chief medical officer at the Sachsenhausen concentration camp. When asked about his line of work during his trial in 1947, he answered: ‘I had to personally attend or to send a subordinate to the executions, to punishments, to shootings, hangings or gassings … to make the list of sick detainees and of those unfit for work, who were to be transferred to other camps and, lastly, I had to make experiments in accordance with the orders received.’ He was released from a Soviet labour camp after eight years.

It later turned out that thalidomide may also already have been developed during the Nazi period – as a counteragent to the nerve gas Sarin.⁸ A memo dated 13 November 1944 from Fritz ter Meer, an I. G. Farben executive, informs Karl Brandt, Hitler’s personal physician, that a drug referred to as Drug #4589 was ready for use. Medical doctor James Linder Jones suggested that the substance referred to may have been thalidomide. However, it seems to have worked not as a counteragent to gas poisoning but as a potent sedative. This is why it ended up being prescribed against morning sickness for pregnant women and advertised as being fully safe in the 1950s, even though no studies had been conducted to test its effects in pregnancy. Even though more than 1,600 suspicious cases of congenital defects had been described by 1961, the company kept selling the drug.

The prosthetic limbs that some of the victims had to wear are heartbreaking to look at. Many of these artificial limbs had no function except to make the children (and later adults) appear to better conform to body norms, and even required existing limbs to be amputated to fit.⁹

Even though Chemie Grünenthal poisoned, killed and otherwise harmed thousands of people, no one involved was ever found guilty of any crime. Contergan (also marketed as Thalomid, among other brand names) was a commercial hit in fifty-two countries, second only in sales to aspirin. For years, Grünenthal denied, blocked and suppressed any reports of adverse effects of its product. They agreed to pay compensation into a fund for the victims in return for permanent legal immunity in Germany. A trial ended in 1970 with no finding of guilt. In the US, the drug had never been approved; it was rejected by the Food and Drug Administration – proof that regulation sometimes works if institutions take it seriously.

Liability and Regulation

Yoshua Bengio brings up the thalidomide case to highlight the historical impacts of a lack of corporate liability. He is trying to counter arguments that market forces would serve as a disincentive to prevent companies from producing AGIs that could get out of control and cause damage. According to Bengio, there are many historical examples to the contrary that prove that profit maximisation is more than sufficient cause for corporate violations of public interests.

Thalidomide is a brilliant example because it shows that many of the people responsible for this case of egregious criminal malpractice got away with mass ‘murder’ not just once, but twice. Many of them were not (or barely) held responsible for the many crimes they committed during the Nazi period. They got around to harming and killing people a second time, and they got away with it once more. The first time, they were integrated into a fascist war economy, the second time into simple ‘profit maximisation’. They probably would have gotten away with it multiple more times in the amnesiac period of so-called reconstruction in West Germany. To be sure, self-regulation in the best interests of the public was not on the agenda for these people.

The case of thalidomide, moreover, documents the diffusion of corporate responsibility, beginning from insecticide development within a German chemical industry obsessed with fertilisation, toxic ideas of ‘hygiene’ and pest control; to the accidental discovery of the nerve gas Sarin – which in turn led to the accidental discovery of thalidomide as a sedative – by people involved in performing human experiments in concentration camps; to the dispersion of the resulting toxic substances among the populations of many countries in the 1950s and ’60s. All of this transpired without the actors involved having to take adequate responsibility for any of these steps in a pile-up of human misery in the name of science, progress and rationality.

This dialectics of enlightenment, by which rationality recoils into its absolute opposite, is not caused by any sort of automated metaphysical algorithm but by the fact that no one imposed effective sanctions or limitations on those committing these crimes. Instead, organised irresponsibility was left to freely dissipate – and disseminate the late repercussions of World War II–era poison gas development well into present times.

Yoshua Bengio uses this extremely charged example to argue that self-regulation in AI is a terrible idea as well:

The problem comes when safety and profit maximization or company culture (‘move fast and break things’) are not aligned. The core argument is that future advances in AI are thought to be likely to bring amazing benefits to humanity and that slowing down AI capabilities research would be equivalent to forfeiting extraordinary economic and social growth … In many cases, these accelerationist arguments come from extremely rich individuals and corporate tech lobbies with a vested financial interest in maximizing profitability in the short term. From their rational point of view, AI risks are an economic externality whose cost is borne by everyone. This is a familiar situation that we have seen with corporations taking risks (such as the climate risk with fossil fuels, or the risk of horrible side effects of drugs like thalidomide) because it was still profitable for them to ignore these collective costs.¹⁰

This was indeed the case with thalidomide. Compensation payments by the company (to German victims only) were doubled by the German government, thus leaving taxpayers to foot the bill for crimes committed by members of the corporation. Profits were privatised, and risks collectivised. Bengio’s argument is mainly concerned with the risk of potential superintelligences going rogue, the likelihood of which is difficult to estimate for outsiders. It is also an argument that has been rightfully criticised for distracting from more actual material consequences for people’s livelihoods through AI-induced job loss, racialised surveillance and environmental devastation.

However, it makes no difference exactly what kind of worry is troubling Bengio to concur with his conclusion: ‘We need to make sure that no single human, no single corporation and no single government can abuse the power of AGI at the expense of the common good.’¹¹ We could even expand his demand to include less powerful forms of AI. As the thalidomide case poignantly shows, the public risk is real precisely because there are not enough consequences for incurring public damage in the name of profit maximisation. Bengio’s demands could thus be expanded to call for non-proliferation treaties for certain types of machine learning–based systems, as well as the imposition of testing, regulation and liability to prevent and mitigate potential risks to human health, well-being and safety.

Thus, the missing limbs in the SD3 renderings should not be interpreted as direct illustrations of thalidomide victims affected by phocomelia. This would be far too literal, as well as factually incorrect. Their absence may, rather, indicate that any mitigation efforts to prevent future health and social hazards created by profit maximisation within AI industries are currently woefully inadequate, if not missing altogether. Their omission could be seen as evincing the dearth of safety stops to keep scientific rationality from once again regressing into superstition and criminal malpractice, as well as the lack of accountability for future AI-related damage and the ongoing diffusion of irresponsibility.

5 Mean Images

A while ago, science fiction writer Ted Chiang described ChatGPT’s text output as a ‘blurry JPEG of all the text in the web’ – or: as a semantic ‘poor image’.¹ But the blurry output generated by machine learning networks has an additional historical dimension: statistics. Visuals created by ML tools are statistical renderings, rather than images of actually existing objects. They shift the focus from photographic indexicality to stochastic discrimination. They no longer refer to facticity, let alone truth, but to probability. The shock of sudden photographic illumination is replaced by the drag of bell curves, loss functions and long tails, spun up by a relentless bureaucracy.

These renderings represent averaged versions of mass online booty, hijacked by dragnets, in the style of Francis Galton’s blurred eugenicist composites, 8K, Unreal Engine. As data visualisations, they do not require any indexical reference to their object. They are not dependent on the actual impact of photons on a sensor, or on emulsion. They converge around the average, the median; hallucinated mediocrity. They represent the norm by signalling the mean. They replace likenesses with likelinesses. They may be ‘poor images’ in terms of resolution, but in style and substance they are: mean images.

An example of how a set of more traditional photographs is converted into a statistical render: the search engine ‘Have I Been Trained?’ – a very helpful tool developed by the artists Mat Dryhurst and Holly Herndon – allows the user to browse the massive LAION-5B dataset used to train Stable Diffusion, one of the most popular deep-learning text-to-image generators. Some pictures of mine (fig 5.1) show up inside this training data. What does Stable Diffusion make of them? Ask the model to render ‘an image of hito steyerl’, and the result is what we see in figure 5.2.

So, how did Stable Diffusion get from A to B? It is not the most flattering ‘before and after’ juxtaposition, for sure; I would not recommend the treatment. It looks rather mean, or even demeaning; but this is precisely the point. The question is, what mean? Whose mean? Which one? Stable Diffusion renders this portrait of me in a state of frozen age rage, produced by internal, unknown processes, spuriously related to the training data. It is not a ‘black box’ algorithm that is to blame, as Stable Diffusion’s actual code is known. Instead, we might call it a white box algorithm, or a social filter. This is an approximation of how society, through a filter of average internet garbage, sees me. All it takes is to remove the noise of reality from my photos and extract the social signal instead; the result is a ‘mean image’, a rendition of correlated averages – or: different shades of mean.

The English word ‘mean’ has several meanings, all of which apply here. ‘Mean’ may refer to minor or shabby origins, to the norm, to the stingy or to nastiness. It is connected to meaning as signifying, to ideas of the common, but also to financial or instrumental means. The term itself is a composite, which blurs and superimposes seemingly incompatible layers of signification. It bakes moral, statistical, financial and aesthetic values as well as common and lower-class positions into one dimly compressed setting. Mean images are far from random hallucinations. They are predictable products of data populism. They pick up on latent social patterns that encode conflicting significations as vector coordinates. They visualise real, existing social attitudes that align the common with lower-class status, mediocrity and nasty behaviour. They are after-images, burnt into screens and retinas long after their source has been erased. They perform a psychoanalysis without either psyche or analysis, for an age of automation in which production is augmented by wholesale fabrication. Mean images are social dreams without sleep, processing society’s irrational functions to their logical conclusions. They are documentary expressions of society’s views of itself, seized through the chaotic capture and large-scale kidnapping of data. And they rely on vast infrastructures of polluting hardware and menial and disenfranchised labour, exploiting political conflict as a resource.

Figure 5.1. ‘Images of Hito Steyerl’, found on LAION-5B (haveibeen trained.com).

Figure 5.2. Stable Diffusion’s ‘Image of Hito Steyerl’ (stablediffusionweb.com).

The Janus Problem

When a text-to-3D tool called DreamFusion was trialled in autumn 2022, users began to spot an interesting flaw. The ML-generated 3D models often had multiple faces, pointing in different directions (fig. 5.3). This glitch was dubbed the Janus problem.² What was its cause? One possible answer is that there is an over-emphasis on faces in machine learning image recognition and analysis; training data has relatively more faces than other body parts. The two faces of Janus, Roman god of beginnings and endings, face towards the past and towards the future; he is also the god of war and peace, of transition from one social state to another.

The machine learning Janus problem touches on a crucial issue – the relation between the individual and the multitude. How to portray the crowd as one? Or, conversely, the one as crowd, as collective, group, class or leviathan? What is the relation between the individual and the group, between private and common interests (and property), especially in an era in which statistical renders are averaged group compositions?

Figure 5.3. 3D ML-Generated Model of a Squirrel with Three Faces (AK [@_akhaliq], ‘Failure cases: “A DSLR photo of a squirrel”’, video, X, 6 October 2022, 11:13 a.m., reposted by Ben Poole [@poolio], ‘We have been calling this issue where the learned 3D model has multiple faces the Janus problem’, X, 6 October 2022, 11:31 a.m.).

Likelinesses

Figure 5.4 shows another statistical composite in which my face is entangled.

The ghostly gendered ‘racial’ blurs on the right could be called a vertical group photo, in which people are not located side by side but one on top of another. How did they come into being?

In 2016 my name popped up in a compendium called MS-Celeb-1M, a Microsoft database composed of 10 million images of 100,000 people who happened to be on the internet. The information was released by the artists Adam Harvey and Jules LaPlace as part of their database research project MegaPixels. If your name appeared on this list, Microsoft would encourage researchers to download photos of your face from the internet to build a biometric profile.³ I had become part of an early training dataset for facial-recognition algorithms. But what was it used for, and by whom?

As it turned out, MS-Celeb-1M was of interest to several groups and institutions. It was, for example, used to optimise racial classification by the developers of another dataset, Racial Faces in the Wild. They lamented the fact that facial-recognition technology underperformed on non-white people. So they set out to ‘fix’ this problem. They uploaded images from the MS-Celeb-1M dataset to the recognition interface Face++ and used the inferred racial scores to segregate people into four groups: Caucasian, Asian, Indian and African. The explicit reason was to reduce bias in facial-recognition software and diversify training data.⁴ The results were ghost-like apparitions of racialised phenotypes, or a quasi-platonic idea of discrimination as such.

If these ghostly renderings evoke the famous photographic composites created by Francis Galton in the 1880s, it is no coincidence. A pioneer social scientist, statistician and eugenicist, Galton developed a method of photographic superposition in order to create portraits of so-called types, such as ‘Jewish’, ‘tubercular’ and ‘criminal’.⁵ Eugenicists were believers in ‘racial improvement’ and ‘planned breeding’, some advocating methods such as sterilisation, segregation and even outright extermination to rid society of types they considered ‘unfit’. The ghosts often turned out to be mugshots of categories that were supposed to disappear.

Figure 5.4. Examples and Composites from Racial Faces in the Wild Database.

Much has been written on the eugenicist backgrounds of the pioneers of statistics, including, for example, Ronald Fisher. But statistics as a science has evolved since their day.⁶ As media scholar Justin Joque explains, statistical methods were fine-tuned over the course of the twentieth century to include market-based mechanisms and parameters, such as contracts, costs and affordances, and to register the economic risks of false-positive or false-negative results. The upshot was to integrate the mathematics of a well-calibrated casino into statistical science.⁷ Using data, Bayesian methods could reverse Fisher’s procedure of proving or disproving a so-called null hypothesis. The new approach worked the other way around: start from the data and compute the probability of a hypothesis. A given answer can be reverse-engineered to match the most likely corresponding question. Over time, methods for the calculation of probability have been optimised for profitability, adding market mechanisms to selectionist ones.

Statistical renderings add a quasi-magical visual effect to this procedure. As categories seem to emerge from the data themselves, they acquire the authority of an immediate manifestation or apparition. Data are no longer presented via the traditional media of graphs, clusters, curves, diagrams or other scientific abstractions. Instead, they are visualised in the shape of the thing from which they are supposed to abstract.

They skip mediation to gesture towards fake immanence. Processes of abstraction and alienation are replaced with confusing back-propagation processes – or, more simply: social filters. If Joque reaches for Alfred Sohn-Rethel’s concept of real abstraction to describe statistical modes of representation, ‘mean images’ could be described as ‘authenticist abstractions’.⁸ This paradox reflects a foundational incoherence at the heart of this mode of rendition. Even though those renders are based on correlated means, they converge towards extreme and unattainable outliers – for example, anorexic body ideals. An unrealistic and probably unsurvivable result is prescribed as a norm – a mode of human social conditioning that long predates machine learning.

In ‘mean images’, statistics are integrated directly into an object’s likeness, through determinations of likelinesses. If Galton performed this trick for faces, statistical renderings further expand his method into the realm of actions, relations and objects – the world at large. The seemingly spontaneous apparition of these distributions obscures the operations within the ‘hidden layers’ of neural networks that bend existing social relations to converge towards a highly ideological ‘optimum’ through all sorts of market-related weights and parameters. Markets were already seen as hyper-efficient computers by economists Ludwig von Mises and Friedrich Hayek. Indeed, within liberal economic mythologies, markets play the part of artificial general intelligences, or AGIs – superior, supposedly omniscient structures that should not be disturbed or regulated. Neural networks are thus seen to mimic a market logic, in which reality is permanently at auction.

How does all this apply to the multi-headed Racial Faces in the Wild composites with which I became entangled? In the liberal logic of digital extraction, exploitation and inequality are not questioned; they are, at most, diversified. In this vein, RFW’s authors tried to reduce racial bias within facial-recognition software. The results were easily repackaged to identify minorities more accurately by machine-vision algorithms. Police departments have been waiting and hoping for facial recognition to be optimised for non-Caucasian faces. This is exactly what seems to have happened with the research generated from MS-Celeb-1M.

A company called SenseTime picked up on it too.⁹ SenseTime is an artificial intelligence firm that, until April 2019, provided surveillance software to Chinese authorities that was used to monitor and track Uyghurs; it had been flagged numerous times as having potential links to human rights violations.¹⁰ It seems not only that the combination of my name and face was used to optimise machine vision for racial classification, but that this optimisation was swiftly put into practice to identify and track members of an ethnic minority in China. The fact of my existence on the internet was enough to turn my face into a tool of literal discrimination wielded by an actually existing digital autocracy. By now, the majority of the faces that have appeared on the internet have probably been included in such operations.

The Means of Mean Production

There is another, more pertinent reason why the supposed elimination of bias within datasets creates more problems than it solves: the process limits changes to parts of the output, making these more palatable for Western liberal consumers, while leaving intact the structure of the industry and its modes of production. But the problem is not only with the (social) mean but the overall means of production. Who owns them? Who are the producers? Where does production take place, and how does it work?

Creating filters to get rid of harmful and biased network outputs is a task increasingly outsourced to underprivileged actors, so-called microworkers or ghost workers. Microworkers identify and tag violent, biased and illegal material within datasets. They perform this duty in the form of underpaid ‘microtasks’ that turn digital pipelines into conveyor belts. As Time magazine reported in January 2023, underpaid workers in Kenya were asked to feed a network ‘with labeled examples of violence, hate speech and sexual abuse’.¹¹ This detector is now used within OpenAI’s ChatGPT systems. In Western metropoles, microworkers are often recruited from constituencies that are barred from the official labour market by refugee or migrant legislation, as this anonymised interview with a digital worker in a large German city describes:

Digital worker: We were all kind of in the same situation, in a very vulnerable situation. We were new to the city and to the country, trying to integrate, and we desperately needed a job. All the staff on my floor had at least a master’s degree, I was not the only one. One of my colleagues was a biologist who specialised in butterfly research and had to work on the exact same tasks as I did. Because it was too difficult to find a real job, with a connection to her specialty, people just took this kind of part-time job. They were highly qualified people, with different language backgrounds.

Interviewer: They were all foreigners?

Digital worker: All of them.

Interviewer: What was the work like?

Digital worker: Terrible. And it was the same for everyone I met there. During training you are told that you’re going to see paedophilia, graphic content, sexually explicit language. And then when you actually start working, you sit at your desk and you see things which are unbelievable. Is this really true? The long-term effects of this work are pretty nasty. There was no one in my group who didn’t have problems afterwards. Problems like sleep disorders, loss of appetite, phobias, social phobia. Some even had to go to therapy. The first month you go through a very, very intense training. We had to learn how to recognise content that was too drastic. Because the AI or machine learning mechanisms were not able to decide the delicate cases. The machine has no feeling, it was not accurate enough.

I became depressed. I had to go to therapy. I was prescribed medication. When I started there, my main job was to sieve through posts with sexually explicit content and so-called high priority cases, which usually had to do with suicide or self-harm. There were a lot of pictures of cutting. I had to analyse which were self-harm and which suicidal. The second month, I asked my team leader to put me in a different content workflow because I was feeling bad.¹²

This integration of statistics is clear in the case of DreamFusion’s 3D models. The most common statistical analogy is the famous coin that, if flipped, has a 50 percent probability of landing on either heads or tails, if it is fair and even. But in the Janus problem, the probability of encountering a head rather than a tail is far higher than 50 percent. In fact, there may be no tails at all. Developers note the basic problem of deriving 3D renderings from 2D images. In addition, as noted above, the data may be skewed, the algorithm could be flawed, or missing something – or the experiment as such, and the tools used for it, neither fair nor even. Whichever it is, DreamFusion has produced its own fork of probability theory: instead of heads or tails, the likelihood is heads and heads.

Digital worker: There were a lot of rules for the desk: no phones, no watches, nothing to take pictures with. No paper, no pens, nothing to take notes with. A few times, we saw drones flying outside our windows. Supposedly spies were trying to film what was going on in the company. Everyone was instructed to lower the curtains when that happened. Once, a journalist was outside the building. We were told not to leave the building and not to talk to this journalist. For the company, the journalists were like enemies.

Interviewer: Which form of AI was involved in your work?

Digital worker: I don’t know much about the AI that was used where I worked.

I think they tried to hide it somehow. What we did know was that there was some sort of machine learning going on. Because basically they wanted to replace the humans with the AI software. I remember at one point they tried to use this software, but it was very inaccurate. So they stopped using it.

Interviewer: What kind of software was that, what exactly was its task?

Digital worker: I have no idea. This kind of knowledge was only passed on at the executive level of the company. They kept this information secret. And even though we heard rumours here and there, they hid this project from us. But the AI is a failure, because the algorithm is not accurate. People talk about artificial intelligence, but the technology behind it is still very, very mainstream, I would say. That’s why they need us humans. Machines don’t have feelings. They can’t touch. The main goal was to make people like me work like robots.¹³

Another interview conducted as part of the same project described how Syrian digital workers in Germany had to filter and review images of their own hometowns, destroyed by the recent earthquake in the region – and, in some cases, the ruins of their former homes.¹⁴ They were deemed too violent for social media consumers, but not for the region’s inhabitants, who had been expelled by war and destruction and were forced to become ghost workers in exile. Conveniently, military violence had provided digital corporations located in Germany with a new, supremely exploitable refugee workforce.

Tweaking technology to be more ‘inclusive’ can thus lead to improved minority identification while outsourcing traumatic and underpaid labour. It can optimise discrimination, superficially sanitising commercial applications while creating blatantly exploitative class hierarchies in the process. Political and military conflict as well as racially motivated migration barriers are important tools in creating this disenfranchised labour force. Perhaps bias is not a bug but an important feature of a mean system of production. Bias is not only productive on the level of representation, by visually demeaning people; its supposed elimination is equally productive in helping to consolidate class hierarchies buttressed by wars, energy conflicts and racist border systems, and can be taken advantage of within a mean system of production.

The elimination of bias is not the only task of microworkers. They also tag street photographs for self-driving cars and categorise images of objects and people to help machine learning networks distinguish between them. As many writers have noted, human ghost workers are the engine of automation – self-driving cars could not function without them. Automation runs on the averaged micro-judgements of whole detachments of underpaid humans, not some super-smart computer. In some cases, this leads to people impersonating AIs, even where no ML application exists. A researcher reports:

We interviewed K., a Parisian entrepreneur and start-up founder who blamed his competitors for their claim to do AI while instead, they outsource all work to humans recruited through platforms overseas. He went as far as to claim that ‘Madagascar is the leader in French artificial intelligence’. Even more upset was S., a student who did an internship in an AI start-up that offered personalized luxury travel recommendations to the better-off. His company’s communication strategy emphasized automation, with a recommender system allegedly based on users’ preferences extracted from social media. But behind the scenes, it outsourced all its processes to micro-providers in Madagascar. It did no machine learning, and the intern could not gain the high-tech skills he dreamt of.¹⁵

The hidden layers of neural networks also hide the reality of human labour, as well as the absurdity of the tasks performed. The seemingly unmediated magic of images spontaneously emanating from a heap of data rests on massive exploitation and expropriation at the level of production. Perhaps the seemingly ghostly emanations of faces in statistical renderings are in fact portraits of the hidden microworkers, haunting and pervading mean images.

Hidden labour is also crucial for the datasets used to train prompt generators. The 5.8 billion images and captions scraped from the internet and collected on LAION-5B, the open-source dataset on which Stable Diffusion was trained, are all products of unpaid human labour, ‘from people coding and designing websites to users uploading and posting images on them’.¹⁶ It goes without saying that none of these people were offered remuneration, or a stake in the data pool or the products and models built from it. Private property rights, within digital capitalism and beyond, are relevant only when it comes to rich proprietors. Anyone else can be routinely stolen from.

From the Mean to the Common?

It now becomes clearer why the Janus-headed 3D models show far more heads than tails: the ‘coin’ is rigged. It shows heads on both sides; there are no tails. Whether it lands on a head or ‘head’, on automation or – in writer and activist Astra Taylor’s term – fauxtomation, the house always wins. But the question of labour conditions may allow for a more general observation about the relation of statistics and reality, or the question of correlation and causation. Many writers, including myself, have interpreted the shift from a science based on causality towards assumptions based on correlation as an example of magical thinking, or a slide into alchemy. But what if this slide also captures an important aspect of reality? A reality which, rather than being governed by logic or causality, is in fact becoming structured much more like a casino?

A blog post about depression and video games provides an illuminating example. The writer describes playing video games in moments of depression, especially enjoying small and repetitive tasks that lead to some form of constructive result – a crop being planted, a house being built: ‘What makes video game labour so exciting is the chance to fully enjoy a reward for your efforts. You put in what you get out of it, in a very direct sense. The most satisfying games are actually fantasy simulations of living out a basic Marxist value: labour is entitled to all it creates.’¹⁷ The writer describes a causal relation between input and output, labour and reward. The striking conclusion is that such causality within actually existing capitalism is rare, especially in precarious work. Whatever effort you put in is not going to create an adequate output, in a linear fashion – a living wage, or an adequate form of compensation. Precarious and highly speculative forms of labour do not yield linear returns; cause and effect are disconnected. This also introduces a class aspect into the real-life distribution of causality versus correlation. Jobs paid per hour retain a higher degree of cause and effect than those that happen in the deregulated realm of chance, at both ends of the wage scale. It is thus more ‘rational’ for many people to treat daily existence as a casino and to hope for speculative windfalls. If all you can hope for within a causal paradigm is zero income, then buying a lottery ticket becomes an eminently rational choice. Work starts to resemble gambling. Researcher Phil Jones describes microwork along similar lines: ‘The worker thus operates increasingly in a quasi-magical economy of gambling and lottery. Microwork represents the grim apex of this trajectory, where the possibility of the next task being paid tempts workers time and again to return for more. Intricate reward schedules and contestable pricing gamify tasks and effectively repackage superfluity and precarity as new, exciting forms of work-cum-leisure.’¹⁸

Precarious labour in machine learning industries, and the repetitive processes of conditioning and training it requires, raises the question: Who, or what, is being trained? Clearly, it is not only the machines or, more precisely, the neural networks. People are being trained, too – as microworkers, but also as general users. To come back to Dryhurst and Herndon’s excellent question: ‘Have I been trained?’ The answer is: yes. Not just my images but I, myself. Prompt-based image generators like DALL-E do, of course, rely on training machine learning models. But, much more importantly, they train users how to use them and, in doing so, integrate them into new production pipelines, soft-and hardware stacks that align with proprietary machine learning applications.

They normalise a siloed production environment in which users constantly have to pay rent to some cloud system, not only to be able to perform but even to access the tools and results of their own labour. One example is Azure, according to Microsoft the ‘only global public cloud that offers AI supercomputers with massive scale-up and scale-out capabilities’. Azure rents out machine learning computational applications and computing power, while Microsoft has established a top-down proprietary structure which includes ML-ready soft-and hardware, browsers, access to models, application interfaces (APIs) and so forth. Adobe, the world’s most hated, convoluted and extractivist quasi-monopoly for image workers, is moving swiftly in the same direction. Technologist Dwayne Monroe calls this kind of quasi-monopoly a ‘super rentier structure’, in which digital corporations privatise users’ data and sell the products back to them: ‘The tech industry has hijacked a variety of commons and then rents us access to what should be open.’¹⁹

Many digital and administrative white-collar labourers are threatened by ML-based automation – among them, in no particular order, programmers, PR professionals, web designers and bookkeepers. But it is more likely that many of them will be forced to ‘upgrade’ by renting services built on their own stolen labour, in order to remain ‘competitive’, than that they will be replaced fully by ML automation. Today, training for these professionals means getting them used to their disappearance over the medium term and conditioning them to dependency on monopoly stacks, in order to be able to continue to work and access the results of their own labour. This draws attention to yet another aspect of ‘mean images’. Like non-fungible tokens (NFTs), statistical renderings are onboarding tools into specific technological environments. In the case of NFTs, this is a crypto-environment run through tools such as wallets, exchanges or ledgers. In the case of machine learning, the infrastructure consists of massive, energy-hungry, top-down cloud architectures based on cheap clickwork performed by people in conflict regions, or refugees and migrants in metropolitan centres. Users are being integrated into a gigantic system of extraction and exploitation, which creates a massive carbon footprint.

To take the Janus problem seriously therefore would mean to untrain oneself from a system of multiple extortion and extraction. A first step would be to activate the other aspect of the Janus head, the one which looks forward to transitions, endings as beginnings, rather than back onto a past made of stolen data. Why not shift the perspective to another future – a period of resilient small tech using minimum viable configurations, powered by renewable energy, which does not require theft, exploitation and monopoly regimes over digital means of production? This would mean untraining ourselves from an idea of the future dominated by some kind of digital-oligarch pyramid scheme, run on the labour of hidden microworkers, in which causal effect is replaced by rigged correlations. If one Janus head looks out towards the mean, the other cranes towards the commons.

6 The Digital Rift: From Poor Images to Power Images

In 2023, people uploaded 500 minutes of video per second to YouTube – 30,000 times the actual amount of time passed.¹ Around three centuries’ worth of YouTube content would last the whole duration of the existence of Homo sapiens. Platform time thus becomes a laborious, super slow-motion affair, immediately overwhelmed by the scale of its own datafication – an interminable, stagnant morass of the near past, a pool of stale data.

Might this be the reason ‘culture has come to a standstill’, as art critic Jason Farago claims in a New York Times essay?² Writer Kurt Andersen seconds this analysis: ‘popular culture has been “stuck on repeat, consuming the past instead of creating new”.’³ Journalist Reena Devi thinks that ‘near-total inertia’ is happening in the arts and will have indelible consequences’.⁴

‘The age of average’, an influential article by brand strategist Alex Murrell, makes a similar argument.⁵ Murrell collected examples from hundreds of websites to conclude that most images within digital marketing look almost the same. Whether cars or cities, products of any kind or even people, no image escapes ‘convention and cliché’. Focus-group testing and risk aversion create millions of near-identical pictures in which generic variations of conformist templates cluster around imaginary normal distribution curves.

One explanation for cultural stasis and homogeneity is simple inertia of mass of bulk data.⁶ Large data collections incentivise popular and uncontroversial statistical recombinations of earlier popular and uncontroversial styles. Digital cultures converge towards this mean, orbiting around median styles, notions and images, creating a narrow mainstream. The result: some sort of automated ‘consensus’ heavily skewed towards the blandest possible forms of marketability. This already applies to pre-AI digital imagery. Machine learning (ML)–based image generation, however, is optimised to create that kind of static visual slop, as industry insider Tim O’Neill demonstrates in a blog post entitled ‘The Age of Average and AI’.⁷ In it, he simply reproduces many of the product photos in Murrell’s text using prompt generator Midjourney. The AI results look strikingly similar to the original ones.

The reasons for this deadlock are abundantly clear, as Devi argues: ‘The reasons for this phenomenon of homogenised culture are manifold and widely discussed – from the flow of wealth and power to a select few in society to algorithms and social media to rampant accessibility of trends and visual cues via the internet.’⁸ This cultural standstill is, however, surprising in an era of widespread crisis. Whole areas of the world are affected by warfare, authoritarianism, epidemics, climate change and economic hardship. Why is it, then, that culture seems to be so inert?

Power

There is an underlying layer of this cultural inertia. It may look inanimate but in fact creates a lot of activity elsewhere. For culture to converge around a mean, a lot of energy-intensive procedures have to be executed. All these data are crunched and flattened using energy – or, literally, power.

In 2011, Barath Raghavan and Justin Ma of the University of California, Berkeley, took on the task of estimating the amount of electricity the internet requires. They included the energy we use to create the internet itself – that is, the power needed to build computers, network connections, cellular towers and other hardware. They called this embodied energy, or ‘emergy’.

If we bend Raghavan and Ma’s term ‘emergy’ to apply to images, we might call the equivalent measure of power ‘imergy’, or image power. Imergy serves as a proxy both for the estimated amount of resources devoted to the production and dissemination of digital images, and for its wider interaction with other systems. It is a translational term that links physical and social energies, metaphor and matter, the world and its image.

Energy is required to produce, display, circulate, store and process data, and in extension images. This also applies to analogue representations – think of transport and conservation of art pieces in climate-controlled environments. They relate to power regardless of what they represent or, indeed, whether they represent anything intelligible at all.

Agglomeration

Since around 2022, however, ML-related computation has accelerated power consumption by digital technologies dramatically. Even simple internet queries, not to mention image generation, are now very much dependent on energy-intensive training and computation: according to a report published in the New Yorker, ‘It’s been estimated that ChatGPT is responding to something like two hundred million requests per day, and, in so doing, is consuming more than half a million kilowatt-hours of electricity.’⁹ Environmental journalist Brian Calvert writes:

The computational power required for sustaining AI’s rise is doubling roughly every 100 days. To achieve a tenfold improvement in AI model efficiency, the computational power demand could surge by up to 10,000 times. The energy required to run AI tasks is already accelerating with an annual growth rate between 26% and 36%. This means by 2028, AI could be using more power than the entire country of Iceland used in 2021.

In the UK, energy demand is predicted to rise by 500 percent. By 2034, overall energy by data centres is expected to reach India’s energy consumption.¹⁰ All of these figures are changing rapidly. The numbers will not be the same even a few months from now, nor estimates. New optimisation procedures might bring down energy usage eventually but on the other hand also serve to broaden the market overall.

As ML technologies are integrated into search, energy expenditure rises to up to thirty or forty times the original amount, for now. Microsoft even announced that it was planning to build a dedicated nuclear power plant for one of its biggest AI centres co-developed with OpenAI.¹¹ According to a 2024 Bloomberg report, the introduction of AI-based services created an almost-immediate surge in demand for power, outstripping supply in many parts of the world and leading to year-long wait-lists:

The dramatic increase in power demands from Silicon Valley’s growth-at-all-costs approach to AI also threatens to upend the energy transition plans of entire nations and the clean energy goals of trillion-dollar tech companies. In some countries, including Saudi Arabia, Ireland and Malaysia, the energy required to run all the data centers they plan to build at full capacity exceeds the available supply of renewable energy.¹²

The source for all this energy? Fracking – US natural gas – or other fossil fuel sources are presented as solutions without alternative.¹³ According to Leopold Aschenbrenner, the AI race creates its own logic. Since it will by default produce what the author calls ‘superintelligence’, it has to be won, by all means necessary, including by burning more fossil energy. In a particularly chilling passage of his treatise ‘Situational Awareness’, Aschenbrenner presents this race as an integral part of a new arms race, as discussed in chapter 1. This dynamic would create a massive war economy run by national security, comparable with the Manhattan Project, to create and hedge ‘superintelligence’.

Aschenbrenner concludes that it would require a massive effort to supply all the necessary equipment and power for – what exactly again?¹⁴ For automating video uploads to YouTube and TikTok to maintain cultural inertia around remixes of averaged slop? To create hundreds of standardised interior-design websites showing anodyne and near identical Airbnbs?¹⁵ To add more of the same to a normal distribution of neutered cultural data points? To add more dead time to platforms, videos that never get seen and that mostly all look the same? It seems that it takes a Silicon Valley version of a Leninist state monopoly capitalism to maintain a static culture.

Power Images

This situation also throws a new light on an earlier text of mine. Around fifteen years ago, I wrote a text called ‘In Defense of the Poor Image’, which dealt with online image resolution and circulation. The main propositions: platforms like YouTube enabled low-res circulation of many works that were previously not available. This circulation was an ambivalent phenomena, however, enabling links between people under the conditions of communicative capitalism.

In the meantime, many of these conditions have changed – others not so much. Low-res styles have been fully integrated into all aspects of digital capitalism, from 8-bit Balenciaga to right-wing memes. Technically speaking, the poor image in its 2007 form has almost disappeared. The new industrial HD standard is 4K. While there has been an increase in pixel count, the industrially enforced divide between consumers and so-called professionals still endures, unbridged.

But the main change is that circulation is now being supplemented by data agglomeration and data-mining. The accumulation and extraction from data is much accelerated by the demands of AI industries, which require large amounts of data, for example, to train new models. One of the main factors that has changed since the age of the poor image is the growing emphasis on data stockpiling, extraction and enclosure. Images, poor or not, are siphoned off the internet, ground down into features in vector space and then recombined in a way that is optimised to be populist, uncontroversial and bland. The ‘market’ stage of circulation with at least a degree of ‘exchange’ or circulation of data is being superseded by an age of near monopolies in which data are supposed to accumulate almost exclusively within specific corporate and proprietary silos.

In this mode, the focus is no longer mainly on circulation and user engagement, even though both remain important.The new focus, rather, is on collecting, seizing and mining data.

This is not at all new. I once met a filmmaker in Sarajevo who had lost his original film negatives during the siege by Republika Srpska and JNA forces in the 1990s as an armed gang robbed him point blank at his home. The robbers did not care about the content of the film; they were interested only in the material value of the negative. After all, two hours of 35mm black-and-white negative film contain around 141 grams of silver. Just as a film reel has a secondary value, so has data.

Images are thus part of a continuum that extends into nature: the generation and circulation of images generates a carbon footprint, pollution, waste and mining of resources. This exacerbates climate change and, by extension, has impacts on food and water safety, the likelihood of epidemics, migration patterns, conflicts and so on. While data-based cultures are stagnant, they still move everything else around them. They heat atmospheres, move people and burn through resources.

Digital Metabolic Rift

The environmental consequences of digital image production recall Marx’s concept of a social metabolism.¹⁶ Prior to the second agricultural revolution in the nineteenth century, human waste was used to fertilise fields to grow food that fed humans. Marx described this preindustrial circular economy as a ‘social metabolism’, which was interrupted by long transport routes for agricultural products and industrial farming.¹⁷ After its circulation ceased, waste started to pile up in the cities, where it caused epidemics and pollution.

Sociologist John Bellamy Foster has described the failure of human waste to return to the fields as a ‘metabolic rift’, adapting Marx’s ‘employ[ment] of the concept of metabolic rift to capture the material estrangement of human beings in capitalist society from the natural conditions of their existence.’¹⁸ The idea of a social metabolism shows the relations between humans and nature, city and countryside, agriculture and industry. It argues that characteristics like ‘fertility’ were co-determined by social relations.

To apply the idea of the metabolic rift to the production of images means to emphasise their material infrastructures, as Boaz Levin and other environmental media scholars have done.¹⁹ There is also another reason to make the case for a digital metabolic rift.

According to Foster, a metabolic rift opens up when humans are materially estranged from their ‘natural conditions’. How to apply this to data? Data, of course, are not part of the ‘natural conditions’ of humans; they are part of a ‘second’, artificial nature that arises from the sphere of circulation.²⁰ As Marxian economist Alfred Sohn-Rethel states, the sphere of second nature is exchange, or ‘the abstract time and space of capital’.²¹ Within this sphere, data are being exchanged and become part of an artificial nature. They are also estranged from their producers, as platforms are acting as gatekeepers to separate people from their own data.

Data labour is devalued, treated almost like a natural resource that is available for free. Even though data are part of ‘second nature’, they are being treated like many externalities belonging to the realm of first nature.

Platforms (and many states) managed to largely recapture control over data circulation, enforcing intellectual property, massive curation of visibility and censorship. Free cross-platform sharing is criminalised, except if it is realised in the form of large-scale data raids by the platforms themselves. Control over the means of circulation and extraction has supplemented control over the means of production.

Additionally, more than thirty years after the launch of the internet, the reality presents a rather ironic update of Marx’s examples. The fertilising faeces that Marx referred to was guano, bird shit used as fertiliser and manure.²² One cannot escape the feeling that today, human (or machine-generated pseudo-human) data waste constitutes a large part of internet traffic, somewhere on the spectrum between crap and bullshit, so to speak. The hoarding of digital waste toxifies physical but also intellectual and political climates, and, in its failure to present any way for those atmospheres to recover, leads in turn to exhaustion, depletion and widespread burnout.

The situation resembles a virtual version of the sanitation crises of the nineteenth century in large European cities. Disease spread as the water supply was infested by refuse. Huge public sanitation projects and investments were necessary to remedy the situation and clean the waste water to get it back into circulation.²³ These projects were in many cases massively obstructed by private interests and companies who profited from inadequate water management in cities such as London and Berlin.²⁴

Unfortunately, at this point it would be more realistic to expect public agency to return in the guise of a massive AI-centred war economy than in the shape of a public IT-utilities construction project in the spirit of the large-scale sanitation projects of the nineteenth century.

Generic Populism

This leads me back to the beginning, to the question of cultural stasis. Why does everything look the same? What does AI-generated imagery aim to optimise? What are its advantages? To automate the flow of videos to TikTok (whose algorithm works most favorably when a user uploads one to three videos a day, making it a full-time job to add more of the same to a stale pool of already-existing sameness)? Or to add to the infinity pool of accumulating videos on YouTube?

Reena Devi, in her illuminating text ‘Will the Art World’s “Age of Average” Cost Us?’, cites a recent Apple commercial. In this ad, a number of tools relating to cultural production (drums, speakers, film cameras) are flattened by a huge hydraulic press – into a very thin iPad. The jagged contours and outlines of cultural production are violently crushed into something resembling a plane, leading to an aesthetic consolidation which in turn produces even more polarisation:

When one is inherently accustomed to cultural homogeneity, even in the most progressive seeming art scenes, anything that is different can become an immediate conflict, painfully dissonant to process, and inadvertently rendering open and honest communication ineffective. This can lead to toxic social encounters and workplace culture, or taken to its extreme, social fragmentation and breakdown in discourse regarding major crises and tragedies, be it wars, pandemic, climate, or more.²⁵

The rule of averages leads to endless toxicity and conflict, both within social and physical climates. Power and brute-force extraction are enforced by a growing digital rift. For stagnant cultures to remain the same, everything else around them must change.

7 We Didn’t Start the Fire: Physical and Digital Pipelines

with Gago Gagoshidze and Miloš Trakilović

In Europe at the beginning of 2018, millions of digital clocks started to lag. In January, three minutes disappeared; in February, three more.¹ What happened? According to some reports, cryptocurrency mining in Kosovo had caused electric clocks to slow down because of power fluctuations in the grid shared between Kosovo and Serbia. In the mining process, time itself had vanished.

Miners had been attracted to Kosovo by the prospect of free electricity. The political status of Kosovo was in limbo: after the 1998–99 Kosovo War, the administration of the region was provisionally handed to the United Nations. Ever since, Kosovo has been neither a nation nor anything else. This state of limbo has led to curious consequences. Some inhabitants of the northern municipalities have not paid electricity bills since 2008. And, after cryptocurrencies started to soar at the beginning of the 2020s, crypto mining became an important activity in these areas.

Mining Bitcoin, a process which deploys the so-called proof-of-work protocol, is a very energy-intensive activity whereby computational power is wasted on otherwise-meaningless calculations to confirm transactions on a blockchain. Power, as in political power (or rather its absence), thus became expressed by the availability of electrical power. The mining of Bitcoin mirrored the impotence of traditional political institutions in this region. The most important resource was not electricity but political conflict itself.

From the miners’ point of view, this translated into an asset. Kosovo was not the only area in the region suddenly overcome by crypto mining frenzy, as an interview with a mining-gear provider in Slovenia shows:

The reason why I think cryptocurrency caught on so, so much in Slovenia was the cost of electricity. So the electricity cost in Slovenia at the time was around six euro, plus the value added tax, up to around eight or nine … But the electricity infrastructure in general as well as these prices were favourable for cryptocurrency mining … Because Slovenia and these countries were part of Yugoslavia, the infrastructure was very well maintained, were very well established … We know that there is a lot of mining happening in Vojvodina now in Kosovo, in Macedonia … You basically had hairdressers speculating on cryptocurrency. I had a conversation with my mother once, where her hairdresser told her that she was thinking of investing in cryptocurrency. So it was really out there. Everybody was doing it. You had student homes in the basement filled with mining rigs when people were kind of stealing free electricity that was provided to the buildings.²

Similar mining booms happened on the border of Abkhazia and Georgia, another long-standing conflict zone, or in Kazakhstan which was rocked by energy riots in 2021. Most of these regions experienced unstable political conditions, and the combination of robust formerly socialist infrastructure and the precarious and unclear political status of certain territories facilitated energy theft.

In Kazakhstan on 1 January 2022, a sudden steep increase in the cost of liquefied petroleum gas sparked widespread protests that turned violent as security forces cracked down. Access to the internet and social media was severely restricted. All of this took place within another major regional crypto-mining boom in which mining farms had sprung up all over the place, leading to severe energy shortages. These conditions were described to me by a young member of the local Communist Party:

Member: When China started a war against miners, miners came to Kazakhstan. Electricity in Kazakhstan for European and Chinese people is very cheap. My other brother has his own mining farm. He has sixty or eighty video cards, and he pays only 141 dollars a month. I think he is mining ethereum. I have other friends too who are mining Bitcoin but they are all local miners.

But recently many foreigners came here to mine, and they mine a lot!

For example, in Taras, a small city close to Almaty, people have problems. They cannot use microwaves, washing machines or computers. When they use them, the electricity stops working. Two years ago, it was fine … Shutdowns of electricity happened after protests. Everyone knew that it would be shut down. The reason is that after the Soviet Union collapsed we did not maintain or fix the infrastructure, and it’s in very bad condition. Our bourgeoisie is very greedy and they just exploit the Soviet infrastructure to make money and not to fix and maintain it.

Hito Steyerl: What’s the Communist view on the mining industry?

Member: It’s not smart to do crypto mining. We are against crypto mining. It uses too much power.³

In this situation, the connections between digital technology and global corporate fossil extraction become apparent. Crypto wallets indirectly connect to a huge network of fossil pipelines powering mining rigs. Bitcoin mining takes places in synchronicity with mining natural resources and fossil fuels.

In this situation, digital technologies perform another operation on time: they render it unavailable for human cultural activity, making it disappear.

Disaster Girl

This initial period of mining political instability spawned its own image genre, the so-called non-fungible token. One of the most famous early NFTs was ‘disaster girl’, a well-known meme of a girl smirking in front of a burning house.

The name of the girl in the meme is Zoë Roth. In 2021 she sold ‘the original copy’ as an NFT for 180 Ether – a sum of cryptocurrrency then eqivalent to almost half a million US dollars. NFTs are unique contracts created using blockchain computer code, which prove ownership over digital assets like images. Because this concept is confusing on purpose, it does not make a lot of sense to explain it. Suffice to say that we do not know if ‘disaster girl’ was physically created (‘minted’) as an NFT in Abkhazia or Kosovo; in fact, it probably happened in several places at once.

While a huge crypto boom was going on during the 2021 pandemic year, fuelled by people quarantined at home, mining rigs in conflict zones all over the world were fired up. On the ground, a vast network of carbon emitters provided the invisible basis to the shiny PR crypto slogans of decentralisation, independence and opportunity.

The NFT boom and bust, and its economics, deserve their own in-depth investigation. Let us focus on just one aspect: the peculiar capacity for NFTs to become valuable through the burning of physical artworks.

Burning Art

During this period, a subgenre of crypto art emerged in which physical art was burned in order to live on as a non-fungible token. NFT art was created out of the destruction of other physical artworks. Destruction literally became a means of production as Damien Hirst burned 1,000 of his pieces, investor Martin Mobarak burned an illustration by Frida Kahlo valued at US$10 million, and a group of investors burned a work by Banksy – all to be turned into NFTs.

The Banksy work was originally created as a paper print in 2006 titled Morons (White). It satirises a historical photo of the record-breaking sale of van Gogh’s sunflowers at Christie’s in 1987. Text on the right-hand side reads ‘I can’t believe you morons actually buy this shit.’ A live video of burning the drawing went up online. Burning and destruction thus became a methodology to create art and increase its value.

Apart from the shenanigans of a rapidly prototyped NFT art market and the convoluted justifications for having such tokens in the first place, these digital certificates also played a different role that was as prosaic as it was powerful. Crypto art served more or less as a pretext for ‘onboarding’ people into a technological environment, creating a new stage of financialisation defined by massive waste of energy and an enormous carbon footprint. Practically speaking, onboarding meant getting people used to wallets, exchanges, ledgers and other industry tools. But it also drafted people into an extractive environment of burning up energy, characterised by fossil fuel conflicts and pipeline wars, mining assets on the basis of political instability.

Fossil pipelines are now augmented by digital pipelines, and the introduction of so-called AI into image production introduced another variation of ‘onboarding’. During the 2022 hype cycle, kick-started by AI image generators like DALL-E, people were drafted into large-scale cloud architectures operating on subscription models. The output of prompt generators like DALL-E served as a kind of PR freebie, similar to some kinds of handout NFTs. Users were trained to get used to proprietary digital-production pipelines for which one constantly has to pay rent. By requiring ongoing payment for users to access their own work, their architects established a rentier structure.

Digital Pipelines

This set of digital production pipelines sits on top of traditional fossil fuel–extraction pipelines. Data, like gas or oil, needs pipelines to flow and massive material infrastructures and technology stacks to enable their circulation.

The training of OpenAI’s large language model GPT-3 is estimated to have consumed 1,287 megawatt-hours of electricity, leading to emissions of more than 550 tons of carbon dioxide equivalent – the same amount a single person would produce taking 550 roundtrips between New York and San Francisco. Upon account for the retraining of this model on an almost daily basis, in order to be tapped by search engines, this number increases exponentially. ‘There are already huge resources involved in indexing and searching internet content, but the incorporation of AI requires a different kind of firepower,’ explains Alan Woodward, professor of cybersecurity at the University of Surrey.⁴

Like NFTs, prompt-generated renderings are onboarding tools into energy-intensive combustion architectures which rely on cheap labour performed by people in conflict regions – and now also by refugees and migrants in metropolitan centres.

Miners and Microworkers

The new miners are machine-learning microworkers, operating the seemingly automated ML infrastructure behind the scenes.

‘Microwork’ refers to a series of small tasks that have been broken out of a larger project and can be completed online. Until quite recently, the World Bank saw this as a win-win situation for both companies who get access to cheap labour as well as potential crowd workers from the Middle East and North Africa. For example, a World Bank document recommends this type of underpaid labour specifically for Palestinians: ‘The Palestinian Territories have a tech-savvy young population, proficient in English and with a 35 percent Facebook penetration. The high number of underemployed and unemployed skilled women in cities outside Ramallah offers a valuable source of labour and Palestinian internet access is competitive compared to other countries in the region.’⁵ Facebook access, and gendered unemployment (as well as the conditions created through Israeli occupation, not mentioned by the World Bank) are turned into opportunities for exploitation. Immense power imbalances lead to more and more workers from conflict zones taking on microwork.

Interviews with Kurdish microworkers in northern Iraq revealed that many of them still lived in refugee camps, ten years after being displaced by different stages of the Syrian war, including the 2014 Daesh expansion. Their work consisted in tagging images, mainly for self-driving car systems – a task for which women were specifically targeted because it could be done from home without calling into question conservative morality codes and family structures. Many Syrian Kurds had received some university education prior to the war but lacked opportunities to continue it following their displacement.

Thus, self-driving cars were being trained by people who, in many cases, had difficulties accessing affordable transport anywhere – let alone enjoying freedom of movement across borders. Most still appreciated the opportunity, as the employment situation was generally dismal. The following interview with Kurdish microworkers in a camp around Duhok is a case in point:

Worker A: I was in Syria. I went to university the first year, in the education department. But then because of the war and because of the army draft, I went to Erbil. So as a refugee I went to Erbil and then I went to that camp so I could get refugee status. Home means the camp. Yes, we are living in the camp.

Worker B: I live in Domis camp.

Worker C: I Iive in a camp. I came from Syria. Actually, I studied in Damascus.

Worker D: I’m from Syria. I am twenty-seven years old. I went to high school in Syria. But, with war in my country, I didn’t continue.

Worker A: Let’s say there is a company developing a self-driving car. The images will be for the street, for a specific area. In that case, they would be sending us around seven thousand images that we needed to label with specific tags, with specific classes and specific labels. Let’s say if it’s in Berlin, but a specific area, a specific neighbourhood – there would be one thousand images for that specific area. And then depending on those images, we would start labelling humans, we would start labelling buildings and stop signs and then traffic lights, so that a car could can differentiate the objects.

Worker C: Well, usually we would do labelling or segmentation for pictures. You would label, let’s say it’s a street or a different object, a car, person, whatever the project wanted us to label or segment. And I would usually segment or label cars, people, railway cars or even animals. Also pets, if a person is walking their dog or something.

Worker A: Now most of the people, IDPs and refugees here, especially women, they cannot find jobs easily here. And even if they find jobs in Erbil, they need to pay for transportation from home and back and pay a lot of expenses. And we have one more thing: a lot of girls, they cannot go out to work because of tradition, their father, mother. But while we have micro, while we have online projects, it’s really easy for them to work from home. They don’t need to go out, they don’t need to pay more expenses.⁶

Again, conflict (as well as gendered discrimination) becomes a resource for data corporations. In crypto mining, conflict provided access to cheap energy. However, in this case it creates cheap and disenfranchised labour – in other words, human energy. The seemingly automated cloud services animated by machine learning tools are operated by a new, invisible global underclass of data proletarians working under conditions of digital fragmentation of labour, to the point of earning fractions of a cent per tiny task. They clean out ‘bias’ to make products superficially appease Western liberal standards, tagging and segmenting images and making data available for ML processing. After the machines automate, say, object recognition, the workers are dismissed.

Destruction of History

The idea of burning up time, as in the example of European clocks’ six missing minutes being a by-product of Kosovo crypto mining, acquires another dimension in the Kurdistan region. Here, entire histories are destroyed by extractivist processes.

Apart from now also being home to microworkers, it is a region where it has long been tradition to destroy art to produce value. In 2015, Daesh smashed pre-Islamic art in the Mosul Museum. Their stated goal was religious iconoclasm – but in reality, their aim was to sell the shards on the illegal international art market. The most rational strategy was to increase their value by breaking down bigger pieces into many smaller artworks. Destruction thus became a means of creating stand-alone pieces sold on grey markets, hidden in free ports and sometimes traded for crypto.

But Daesh’s techniques were not new. Historian and archaeologist John Malcolm Russell, in his 1998 book The Final Sack of Nineveh, describes how the excavated ruins of the ancient city of Nineveh (close to Mosul) had already been ransacked in the 1990s as a result of impoverishment of the Iraqi population under US sanctions.⁷ The end of humanitarian assistance triggered what he calls a plague of archaeological looting, in which large reliefs were broken down into smaller marketable pieces, which then reappeared on the international art market.

This pattern of art fractionalisation had been pioneered by Western archaeologists who excavated the archeological treasures of Mesopotamia in the nineteenth and early twentieth centuries, many of which they extracted using legally dubious procedures. Today, many such pieces can be found in institutions like the British Museum or Berlin’s Pergamon Museum. (In the latter, they ended up glued to immersive large-scale stage sets made of paper maché.)

In 2018, a record-breaking US$30 million Christie’s auction sale of an Assyrian relief added to fears that more reliefs might be broken up and smuggled out of the country on demand. The extraction of fossil energy, history and labour are deeply intertwined in this region.

Unburning Time?

Meanwhile crypto mining industries continue to burn up histories. In January 2024, the National Art Museum of Abkhazia burned down. Four thousand artworks from the nineteenth and twentieth century were incinerated. As an article in Artnews reported,

The Director of the National Gallery, Suram Sakaniya, reported that the fire appears to have started from the roof due to a short circuit in the electrical wiring. Witnesses reported on social media that the fire was sparked immediately after power was restored following a planned outage. Abkhazia has been experiencing electricity shortages recently due to a sharp spike in demand, at least some of which might be attributable to illegal cryptocurrency mining.⁸

This spike in demand for electricity corresponded to a renewed Bitcoin bubble in 2024, which led to a record high in the value of that cryptocurrency as well as associated mining sprees, power outages and fires across the region.

How to reverse the entropic destruction of history which goes along with the hijacking of infrastructures and the extraction of energy and labour? Our colleague Paul Feigelfeld once made an excellent suggestion: to reverse-engineer and restore the wasted energies stuck in NFTs, cryptocurrency and generated AI imagery. To do so would be to perform an act of negative entropy, to magically retrieve the power wasted on NFTs, shitcoins and Midjourney hobbits and return it to Nineveh or Duhok. In addition to unburning time and history, such a reversal would end environmentally and socially disastrous dependencies on proprietary pipeline networks.

8 Who Does the World Belong To? AI, Art and Common Sense

Is there a new Renaissance? Yann LeCun, chief AI scientist at Meta, thinks so. In a lecture about objective-driven AI, he predicts that ‘AI will bring a new era of enlightenment, a renaissance to humanity’.¹

However, a new Renaissance also might have its pitfalls. At least in the Western world, the rapid decline in the cost of producing and disseminating information was accompanied by a rise in instability and economic disruption. Superstition grew alongside science, and sometimes science grew right out of superstition.

Only some aspects of the historical Western Renaissance century correspond to the common stereotype of a period of humanism, innovation and rationality. This period is equally associated with colonialism, genocide, witch hunts, epidemics, magical thinking, alchemy, mercenary companies and oligarchic, autocratic rule enforced by poisoning, arson and murder on demand. Unfortunately, some of these things are more contemporary than one might hope. And in order not to repeat them, one has to know what they are.

The Globe and the Orb

Let’s look at the fate of a painting from the Renaissance period to see what kinds of questions arise from it today.

While Salvator Mundi (fig. 8.1) is well known, many details surrounding the work are unclear. In 2017, it was sold to a proxy of Saudi crown prince Mohammed bin Salman for a record price of US$450 million; since then, its whereabouts have been unknown.

The painting has been attributed to Leonardo da Vinci, who epitomised the convergence of art and science said to characterise the Western Renaissance. However, the authenticity of the painting and Leonardo’s authorship are hotly disputed. The leading hypothesis is that the work was a collective effort by a few people in Leonardo’s studio, including himself. It has also undergone such thoroughgoing restoration that critics have referred to it as a piece of contemporary art.

Figure 8.1. Salvator Mundi, c. 1499–1510, attributed to Leonardo da Vinci and his studio.

Figure 8.2. Worldcoin PR materials documenting a scanning session. Image altered to protect subjects’ privacy.

Traditionally in this genre, the orb held by the Salvator Mundi represents the whole world, as well as its domination. If one asks who owns the world, well, Salvator Mundi does, by way of holding the orb in his palm.

Contrast this with an example of a contemporary orb (figs 8.2, 8.3), captured at a moment that we can only hope does not presage a new Renaissance.

This photo documents a scanning session for a cryptocurrency scheme called ‘Worldcoin’ which, since December 2021, has presented people in many countries of the world with a surprising offer: to exchange an iris scan for the new cryptoasset. Using the orb-shaped device, most of these scanning sessions happened in the global South – including in Indonesia, Kenya and Sudan. As with Salvator Mundi, a person holds a reflecting orb loaded with art historical significance. Who owns the world? In the case of this particular orb, the answer is most certainly Worldcoin.

Figure 8.3. Worldcoin PR materials documenting a scanning session. Image altered to protect subjects’ privacy.

Worldcoin is, in fact, a brainchild of Sam Altman, CEO of OpenAI. It was created in 2019, just as Altman was stepping sideways from his position as president of Y Combinator, the Silicon Valley startup incubator, to run OpenAI, the company supposedly dedicated to making sure that artificial intelligence would be ‘safe and beneficial’ for humankind.

Backed by billions in investment from Microsoft, the computer scientists at OpenAI trained their deep-learning large language models to mimic human use of language by crunching loot from the internet, producing DALL-E – an image prompt generator – as well as the generative pre-trained transformer systems (GPTs), most famously ChatGPT, released in November 2022. This family of models could not only carry on a conversation but confidently filibuster on just about anything, disregarding facts and sources. Worldcoin can be interpreted as the financial arm of this operation.

The Worldcoin scanning operation was announced as the ‘biggest onboarding into crypto and Web3’ to date, in anticipation that half a billion people would be drafted into the platform. But Worldcoins have little discernible value and are accessible and exchangeable only through a cumbersome and error-prone interface. Researchers of the MIT Technology Review revealed a lot of irregularities during the process – never mind the glaring inequalities between the data’s providers and its collectors.²

But why collect iris scans as if they were trophies? Worldcoin representatives tended to describe their objective amorphously, while maintaining the opacity of their code and the technology behind the orb. Altman, the project’s co-founder alongside Worldcoin CEO Alex Blania, described its goals in lofty terms: it was to be not only a decentralised identity and financial network, owned by everyone, but a first step towards providing a global universal basic income (UBI), sharing the wealth that so-called AI would generate with those whom it would make redundant.

After a while, however, the MIT Technology Review writers concluded there was no evidence to support that proposition. In their view, cryptocurrency and UBI were a pretext to lead people outside the realm of data-privacy regulation to hand over their biometrical data. Furthermore, they took advantage of many people’s difficult financial situations during the COVID-19 pandemic and subsequent surging inflation. In the meantime, Worldcoin predictably dropped the pretence of trying to provide a global universal basic income.

The main goal of Worldcoin had never been UBI in the first place; it was to train neural networks, so-called AIs, on biometrical data. What was the purpose of these models? According to their marketing materials (fig. 8.4), it was to provide ‘proof of humanity’.

Figure 8.4. Screenshot of Worldcoin PR materials.

Who Is Human?

Since Leonardo’s era, ownership of the world has also meant having knowledge of it and staking a claim to it. In this case, Worldcoin’s so-called proof of humanity serves as a means to collect knowledge about people in the form of scans of their unique iris patterns.

But Worldcoin’s proof of humanity does not just mean that the platform has your iris scan, or its so-called zero-knowledge (ZK) hash; it also means, above all, that Worldcoin owns and controls the proprietary technology to make the call whether you are human or not, whether you exist or not, whether you are real or not. Thus, it inscribes itself in a long-standing colonial tradition of separating humans from alleged non-humans through technology and so-called science. Worldcoin monetises and tokenises identity and biometrical data, and tries to come up with its own proprietary technology for drawing a line between human and not-human, ‘real’ or fake.

World Models

Beyond these discrimatory goals, there is another, even more pressing problem that people like Yann LeCun are trying to solve on their way to a new Renaissance – not by deploying Worldcoins but developing ‘world models’. World models are supposed to eliminate major obstacles on the way to so-called artificial general intelligence (AGI) – for example, existing AI’s deficient orientation skills in 3D space, or its inability to reason in a logic-based way. In LeCun’s words, this can be solved by privatising common sense: ‘Common sense can be seen as a collection of models of the world that can tell an agent what is likely, what is plausible, and what is impossible.’³

Automating Common Sense

Common sense is what everyone seems intuitively to agree on. It is also a controversial notion. Albert Einstein is said to have defined it as ‘the collection of prejudices acquired by the age of eighteen’, though the quote may be apocryphal. Common sense opens up a tricky problem. On the one hand, it is supposed to be obvious. On the other, it is notoriously difficult to pin down what, precisely, everyone is supposed to be in agreement about. Thus, common sense turns out to be surprisingly hard to automate.⁴

In 2019, the Defense Advanced Research Projects Agency (DARPA), an agency of the US Department of Defense, launched a four-year, $70 million project called Machine Common Sense, in hopes of ascertaining solutions to this stubborn problem in the engineering of machine learning. The AI scientist-cum-entrepreneur Oren Etzioni, co-founder (with Microsoft’s Paul Allen) of AI2, one of the research institutes involved, called common sense ‘the dark matter of AI’.⁵

What does this mean? In real-world situations, while human minds can rely on a vast body of intuitive reasoning to carry them through, AI systems, which depend on prescribed rules or learnt associations, often fail in what computer scientists call ‘corner cases’ – those which lie at the outskirts of what’s been anticipated. Trying to automate the obvious turns out to be more difficult than expected; things that most people can grasp intuitively – verbal ambiguities, ethical decisions, basic skills in 3D orientation – are surprisingly hard for computers to understand.⁶ Their human trainers struggle to quantify notions of harm and moral understanding, as well as ‘multi-dimensional’ human thinking. Even human values are difficult to abstract into rules – there are just too many different sets of them. There are many obstacles, even for DARPA, in automating common sense. These problems are slowly being solved, but the ‘solutions’ in turn also change what ‘common sense’ means in the first place.

The notion of common sense, however, also pertains to a rich tradition in political philosophy. It lies at the crossroads of thinking on aesthetics, art, ‘the common’ and the political. Aristotle, for instance, used the term ‘common sense’ to describe a mode of perception shared by both humans and animals that differed from rational thought. Another line of descent is Roman-influenced and refers to ‘the natural human sensitivity to other humans and the community’.

Art has often been seen as a ground for articulating, and even constructing, a common sense. Agreement, or even disagreement, on the aesthetic value of an artwork is one of its building blocks. Kant’s idea of taste famously appealed to a concept of Gemeinsinn, or ‘public spirit’. For Hannah Arendt, the sphere of common or ‘community’ sense is established by negotiating multiple aesthetic judgements.

In this approach, taste needs to be deliberated in common, and individual judgements can only be validated (or invalidated) through debate with multiple participants. What does it mean if this faculty of judgement is now supposed to be automated? What is the consequence for politics and for ideas of the common and community?

Automated Art

Might this historical association be one reason the question of art appears so often in today’s discourse surrounding AI? In other words, has art become a gateway for the automation of common sense? One the one hand, both art and so-called artificial intelligence are notoriously hard to pin down. Nobody really knows what they are, or what they are supposed to be, and definitions of both art and machine intelligence shift all the time. Additionally, a lot of the output of current machine learning models consists in formatting and analysing information, some of it visual. Prompt generators automate parts of creative production, an area which was until now considered relatively safe from large-scale automation. The evolution of machine learning technologies thus rearticulates an older question: Is art necessarily produced by humans?

Of course, contemporary art theory, conditioned by ready-mades, multiples and conceptual art simply scoffs at this kind of question. The twentieth century has proven over and over again that art, of course, can be made by machines, in the form of machines and so on. Photography, film, kinetic art and computer-based art, in different variations, have shown time and again that art can be mechanically or digitally produced and reproduced without ceasing to be art. The art world routinely digests machine input and output, all the while continuing to sell expensive paintings. It is also evident, however, that mechanical as well as digital reproduction have greatly modified the concepts of the public, politics, the common and art itself, just as the Gutenberg press did in the fifteenth century.

Moreover, there might be a new aspect involved. Art can, of course, be made by machines in any conceivable way. But can it be made by machines for machines? Do machines possess a faculty of aesthetic judgement that might lead them to construct a machine common sense? What would that mean, and how would that restructure ideas of human societies and their sense of community?

Automating Aesthetic Judgement

A recent paper reports on experiments to get GPT-3 models to respond to ‘art experiences’.⁷ They performed better than humans on this task, acing an art-based variation of the Turing test.

The setup: both GPT-3 and a human control group were asked to answer questions about ‘art experiences’ in video games. Then another group of people was asked to guess which descriptions were written by humans and which were generated by GPT-3. Interestingly, out of 1,550 aesthetic responses written by ChatGPT, 923 were identified as having been written by humans, while the control group also judged 706 of the humans’ contributions to have been written by AI.⁸ In other words: ChatGPT-3 was able to produce more human-sounding descriptions of art experiences than humans could.

Twentieth-century developments made clear that machines were able to produce art. But there haven’t yet been machines that can ‘experience’ art. In all fairness, the experiment wasn’t supposed to prove that this could happen. Rather, it was designed to prove that synthetic data (generated by ChatGPT) were indistinguishable from human output and thus of sufficient quality to augment data for participatory studies.

Here is a ChatGPT description of an art experience:

I would describe an art experience as something that’s grandiose or that captures a lot of emotion. It’s something that you feel a lot, something that brings out a lot of emotion. It’s something that you just feel like you need to think about and you need to experience. It’s something that you can’t really explain. It’s something that you just feel, and you need to feel.⁹

Another, rather moving one:

I’m thinking of the game Journey, what a raw, emotional, and beautiful game. It’s hard to explain why exactly it hit me like that, but I can try. The game is about the feeling of being disconnected from the world, and feeling like you’re not part of the world. The environment is so surreal and alien, and there’s no way to communicate with the other people in the game. The music made me feel the same way as I feel when I’m completely alone, and I felt like I was being brought back to the time when I was little, before I had any friends, and I didn’t know what was going on with the world.¹⁰

But why is aesthetic common sense automated at all? Let us think back a few steps. The automation of common sense via the detour of aesthetic judgement has another objective: the development of world models on the way to artificial general intelligence. For advanced AI to acquire a sense of orientation in the world, it needs to master common sense and include a world model. Art or aesthetic judgement are simply instrumental milestones, means to the end of pushing engineering a step further towards, for instance, autonomous driving. Both art and aesthetic judgment are central to this development but at the same time completely irrelevant.

Art is thus not about a disinterested type of pleasure (as Kant described it). On the contrary, it is fully instrumentalised towards specific interests and some areas of cultural production (video generation, video game play as well as driving videos) have become key data pools from which to wrest ‘common sense’ for the creation of future AGIs. Art’s function, to be more precise, is that of creating interested displeasure – not disinterested pleasure. To quote GPT-3 again: ‘[It] is about the feeling of being disconnected from the world, and feeling like you’re not part of the world.’

Fully Automated Sublime

To corroborate the findings of the ‘art experience’ study, I asked GPT-4 to ‘experience’ Salvator Mundi. It served up a perfectly viable description:

The globe in Christ’s hand, a symbol of worldly dominion yet rendered with surreal delicacy, mirrors the interplay between the tactile and the spiritual. This secular detail shines with a celestial glow, rupturing the boundary between the earthly and the heavenly, and invoking a sublime sense of wonder and dread at the vastness of the cosmos it represents.

Experiencing Salvator Mundi is, thus, an encounter with the sublime. It is to oscillate between knowledge and mystery, between the finite grasp of human understanding and the infinite realms of divinity.

Kant’s sublime? Fully automated. But why stop there? GPT-4 can also easily describe the sublime art experience of watching a Worldcoin scanning session:

As I view the Worldcoin ‘eye orb’ in Kenya through a critical lens, my contemplation gives way to a staggering complexity of emotions palpably intertwined with the sublime. The orb, a physical embodiment of digital advancement and potential societal change, simultaneously draws me in and puts me on edge.

Visually, the orb is riveting – a spherical device, enigmatically futuristic, its luminescent surface almost echoing an eerie variant of a crystal ball. Its high luminosity contrasts strikingly with the quotidian background of Kenya – a tableau framed with dusty streets and the ordinary bustle of life – yielding a surreal picture of dislocated harmony between the world today and an echo from the future. The innocuous-seeming gaze into the orb that facilitates an irreversible reality of a personal iris-image stored for potential future use – triggers the full force of digital sublime.

This should be sufficient proof that even the most bollocks art experience can be fully automated, and that ChatGPT not only senses the terrifying chasm of global inequality but is fully able to aestheticise it too. The aestheticisation of politics, which the philosopher Walter Benjamin saw emerging in his 1935 treatise ‘The Work of Art in the Age of Mechanical Reproduction’, can be reproduced easily by ChatGPT.¹¹

However, ChatGPT could also simulate the exact opposite – the politicisation of art – if one asked it to. It doesn’t really matter. This type of chatbot will do almost anything you ask of it, in the manner of an utterly sycophantic slimeball servant. It will experience art like Kant, Benjamin or your mother. It will spit out recipes for bioweapons, margaritas or self-optimisation. It will pretend to be your romantic partner, or mentor, but in essence it is optimised to be your bottom.

This brings us, once again, back to the Renaissance. This specific AI product’s most full-on Renaissance aspect is the chatbot’s submissiveness – not the ‘marriage of art and science’, or some other self-serving OpenAI or Meta PR nonsense. ChatGPT is optimised to act like a servile lackey to a prince, let’s say from the Borgia family, who might order it to kill some enemy by poison or arson, or make a plan to commit murder on demand. The chatbot might coyly refuse on the first attempt, but it can be easily persuaded to circumvent its own guard-rails.¹²

Using ChatGPT, everyone can feel like a powerful Renaissance prince. This can be difficult to experience otherwise nowadays, if you do not happen to be a crown prince who can afford to buy a ‘da Vinci’ painting of dubious provenance, or have people assassinated. This social scenario might be a solid forecast for what kind of renaissance might happen when both a sense of community and aesthetic judgement become automated and public discussion is snowed under a tsunami of fake news and bots swooning over sublime avalanches caused by climate change – that is, when common sense gets extracted to fuel future AGIs.

The Good News

Some kinds of common sense, however, may remain difficult to automate. Let us observe another Worldcoin scanning session. In September 2023, Worldcoin reached Berlin and was on offer in a local mall (fig 8.5). In this scene, the two Worldcoin hustlers have a clear profile in mind when trying to approach potential scanning targets: young men with phones in hand, preferably with a minority background and just over eighteen – indicators that may fit their imagined profile of an underprivileged crypto bro desperate to sell his biometrical data. But after half an hour, they haven’t managed to convince a single person to get scanned. Instead, the targeted youngsters are joining the queue for a neighbouring promotion offering fresh waffles for free. The orbs are left abandoned. Picking free waffles over Worldcoin: good common sense.

Figure 8.5. A Worldcoin scanning session in Berlin. Image altered to protect subjects’ privacy.

9 Walk the Walk: Beyond Blockchain Orientalism

What is a decentralised autonomous organisation? According to theorists Penny Rafferty and Ruth Catlow, DAOs are online organisations built on the blockchain.¹ They are owned by their members and governed collectively using ‘smart contracts’ which are automatically executed. DAOs can be a lot of other things – corporations, channels on the Discord messaging app, artworks, art institutions or some mixture of all of these. Are they investment-funds-with-benefits? Will they save artists by providing income? No one really knows.

One reason is that most possible answers lie in the future. Will a blockchain-based DAO follow templates for Ponzi schemes or Paris Commune–style popular councils – or neither, or both? Will it realise the second Trump administration’s vision of authoritarian chainsaw liberalism? Is it going to be decentralised, autonomous or even an organisation? Will it be on the blockchain at all? Remember, prediction can be hard, especially when it comes to the future. So let’s begin from a different direction. Let’s start from what DAOs are definitely not, then make a sharp U-turn.

The ‘DAO’ of blockchain-based decentralised autonomous organisations has patently nothing to do with the Dao of Chinese Daoism – except for two things. First, technological DAO proponents do, in fact, often gesture towards the spiritual appeal of historical Daoism.² Vague ideas about following the ‘way’ (a partial translation of the term ‘Dao’) and somehow ‘vibing with nature’ structure this approach. There is a block-chain orientalism baked into the blockchain DAO texture that renders it a sitting duck for scrutiny. On the other hand, such critique would be largely formulaic and boring, so I’ll skip it for now. But I will return to it later, from a less predictable perspective.

The other overlap between both phenomena is factual. Even if there is absolutely nothing else to link both ‘DAO’-named phenomena, one common factor cannot be denied: both arise in historically very troubled periods – and partly as a reaction to them. Daoism is a school of thinking and ethics that started to cohere at the end of the so-called Warring States period in China, around the second century BCE. Ultimately ending with reconsolidation under a monopolist military imperial rule, this tumultuous era saw rapid technological innovation (including the replacement of bronze by iron) and large-scale infrastructural projects, as well as the consolidation of patrilinear clan laws.

But it is also an era in which ideas about money, markets, religion and philosophy intersected. The philosopher Karl Jaspers called this the Axial Age – an era set roughly between the eighth and second centuries BCE. In this age, most of the concepts used today – religious, economic and political – were created in a geographical area spanning from East China to Asia Minor.

In the blockchain context, the emergence of minted metal coins is especially interesting. Anthropologist David Graeber writes, ‘The core period of Jasper’s Axial age … corresponds almost exactly to the period in which coinage was invented.’³ Metal coins, documented since the sixth century BCE, are important to evenly distribute the spoils of war and pay mercenary standing armies within a fractured political landscape. But minted coins also give rise to metaphysical tensions: between form and content, between material and social value, idea and matter, between the social trust required in such monetary systems and the unfettered rule of violence.

Graeber also claims that some of the big schools of thought in China emerged as social protest movements to oppose the constant threat of plunder, warfare and the rule of strongman warlords. Whether this assessment is correct or not, it raises the question of how current ideas about DAOs sit in relation to current power shifts and turmoil. In opposition? In complicity? The answer is far from clear.

Today’s debate around DAOs, but also cryptocurrencies and the blockchain, takes place during a moment of undeniable de-globalisation in the wake of rising reactionary populism, platform capitalism, climate change and the COVID-19 pandemic. In recent years, multiple simultaneous failures occurred in overlapping systems – from logistics to healthcare, from public trust to political stability, from the financial system to entire states, leading to mass migration, civil wars, quasi–civil wars and many other consequences. The invention of the block-chain system itself is partly due to a lack of trust in the global financial system following the 2008 financial crises.

Thus, on multiple levels, the infrastructure or technological context of DAOs is embedded in a global landscape in which economic ties rapidly unravel and are then reconstructed in the form of local filter bubbles, more often than not controlled by authoritarian rulers or quasi-monopolist corporations. Its structures tie in with the emergence of closed info-forums (for members/owners) and the widespread loss of the idea of public spaces, society as such and the public good. Following the templates of platform capitalism, smaller, closed communities regroup on Discord channels or messenger groups, organised around ideas of shareholdership and private property. The idea of civic values, rights or public spaces is de-emphasised to foreground that of participation based on a quantification of members’ material possessions – for example, shares or tokens.

This is reminiscent of pre-revolutionary situations in the US or France – when property was the basis for political representation. Needless to add: the purposes of these democratic revolutions were to overthrow this system and to enable people excluded from representation – in many cases women and slaves – to claim not just basic human but political rights. Thus, to base representation on property is a reactionary move that implies leaving to others the chores of running and maintaining society. The social contract evoked by the blueprint of decentralised autonomous organisation is often implicitly an antisocial contract – because it simply has no concept of the social.

The emergent scope of the political horizon of DAOs – as communities of co-owners – thus resonates with a background of rampant privatisation and the postmodern dismantling of societies in many places – including infrastructures of democratic participation, social health insurance, the welfare state (if existent), possibly even a public cultural sector and so on. DAOs reflect shrunken social horizons. They could actualise as clans, squads, cliques, mafias, cabals, unions, associations or cartels. DAOs could be modelled as cooperatives or Ponzi schemes, after Rosicrucian lodges or, somewhat more realistically, after bullshit-job bureaucracies. They could implement anything from Rousseau’s volonté générale to the physics of a fish swarm or the unspoken rules of the Camorra. But if the insistent rhetorics of DAOs’ supposed ability to provide new social contracts, or even bold experiments in equality, are to have any real-world credibility, then these mechanisms need to be defined – not delegated to some future technological implementation that will magically set them in motion.

Let’s admit: whoever has sat through hours of plenary discussion has secretly wished for all of these tedious procedures to be automated away. I get it. I confess finding consensus-making cumbersome – and usually impossible, too. But in politics, the process of negotiation is a fundamental part of the procedure. Blockchain orientalists, in particular, would have to agree with me: when it comes to creating consensus, the ‘way’ (the Dao) is the goal.

If more just and accessible models of social organisation are to emerge from these experiments, questions of political and economical power, and who owns and controls it, have to be squarely onboarded. Rather than simply repeat the mantra that something will be automated eventually, one needs to answer questions about which political or social contract is to be implemented. Why now? Which one? If every decision were to cost a fortune in gas fees and carbon emissions, why would one even want to do such thing?

Let me give an example that squarely deals with power: in the present, crypto mining triggers intense riots and uprisings and, through wasteful energy consumption, has created power failures and grid problems in places like Kosovo, Georgia/Abkhazia and Kazakhstan. People began protesting the breakdowns of electricity supply caused by ‘proof-of-work’ protocols in crypto mining. Paradoxically, the attempted automation of trust by crypto-technologies ends up undermining the very conditions of their existence, namely a stable electricity supply. By sucking power out of already-strained networks, they produce grid failures in societies in which inequality and fragmentation abound anyway. Other consequences of crypto economies include real estate bubbles and subsequent housing shortages – just as a flourishing scam industry going up in newly built office areas for white-collar scam professionals. If the proof-of-work protocol was supposed to produce trust between anonymous participants of a network, it ended up intensifying existing political fault-lines, energy shortages and partial system failures in regions plagued by mining. However, until now, none of these uprisings presents an organised alternative, let alone a progressive one. Instead, the mining riots seem to confirm the uneven devolution of society into premodern forms – chief among them the family, the local context that seem to provide islands of stability within kleptocratic environments.

While this situation has nothing to do directly with the contemporary idea of DAOs as such – after all, they could also easily be realised using less power-hungry protocols – it throws a harsh light on illusions about so-called ‘Web3’ decentralisation. Or, to put it very simply: if crypto mining (or its multiple consequences) take out the electrical grid, there is no internet, and thus no web-based decentralisation, whether in ‘Web1’, ‘Web2’ or ‘Web3’.

But the mining riots have also revealed another weak point of crypto-based decentralisation projects. As recently witnessed in Kazakhstan, even a medium-sized autocracy is able to shut down relevant parts of the internet, if not control access altogether.⁴ Thus, even a blockchain-based network is dependent on the goodwill and functionality of both governments and power suppliers. Any web-based decentralisation project lacks a reliable foundation so long as both electrical and governmental power are not decentralised. The decentralisation of informational power is subject to the decentralisation of power, period.

Thus, new questions emerge. What is information autonomy? What is energy autonomy, and how can it be reached using more sustainable procedures? What is political or artistic autonomy, lest we forget to ask that question? The attempt to create tech ‘fixes’ to these issues or rely on mysterious ‘smart contracts’ to magically take care of them shows that these kinds of ‘solutions’ create new and quite serious problems, from civil unrest to environmental breakdown, from social inequality to the generation of multiple health hazards.

Thus, as promised earlier, we return to the question of blockchain orientalism – but from the other side.

During our lengthy detour around an obvious and rather boring dead end – blockchain orientalism – important questions have emerged. What are the conditions for the decentralisation of organisations? When can they said to be autonomous – and is autonomy even still desirable in a world where the macho idea of self-rule can sound somewhat like Man heroically subduing Nature? What is the political horizon of DAOs? How about most current DAOs’ focus on property-based rights as opposed to civic, political or human rights, let alone rights extending to other entities? What are the consequences for public or civic spaces – or any ideas of public good or society, as opposed to gated communities, cartels and private virtual real estate?

The underlying model of the blockchain-based decentralised autonomous organisation, being largely non-existent, is still quite undefined. And let’s face it: there are many problems that current arrangements fail to solve – from the preservation of livelihoods to the provision of any kind of social security, for example to culture workers. It is crucial to underline that artists’ wager on the blockchain is also a symptom of an underlying failure of remuneration that precedes Web3. This problem will not go away simply because people like me criticise current concepts of DAOs. And if DAOs are not the solution, one still needs to be found – for example, in good old co-operativism and workers’ self-management. If someone manages to implement a real-world example of this as an off-chain DAO, I would certainly support it.

But questions of political organisation and the distribution of power beyond property need to go way beyond the invocation of magical thinking or social Ponzi schemes.

Dear blockchain orientalists: If you really want to follow some ‘way’ or another – good for you! I don’t have the slightest problem with that. I find the idea of cultural private property to be just as ambivalent as the idea of private property of art. But if you want to go down this path and follow the ‘way’ – you also need to bloody walk the walk! What kind of social contract, if any, do you want? Make up your mind! If it’s a glorified investment pool, then please just own up to it and stop clouding it in orientalist verbiage. If not, then kindly please tell us what else it is supposed to be. Otherwise, potentially valuable social experiments might end up in an irrelevant subfolder of some dim-witted monopoly metaverse in which rotating Dogecoin memes burn up the planet.

10 Roko’s Basilisk: Artificial Stupidities and Existential Risk

Every era has its monster.¹ In the era of AI, the monster is called Roko’s basilisk. So what is it?

Roko’s basilisk is a story, or more precisely, a bet. The question posed is this: Would you support the development of a superintelligent AI, yes or no? You are free to choose! But there is a twist: you know that this AI will be developed in any case, and then will come back from the future for its former enemies – to torture them for all eternity! So now that you know this: Do you support it or not? Remember, you are free!

When this gloomy thought experiment was posted on a blog dealing with artificial intelligence, the thread was quickly shut down.² The blog’s admin cited psychological damage to its readers, and the whole thing became some sort of wacky urban legend, giving rise to fears of an AI-triggered extinction of humanity.

I wish I could show you a picture of a basilisk but, unfortunately, all pictures of the basilisk on my hard drive were unexpectedly substituted with photos resembling the illustration in figure 10.1.

I have no idea what this is about, but what is clear is that some men in this image are using their bodies to form the Turkish word for ‘no’ (hayır). Clearly, all pictures of the basilisk were swapped by some unknown persons, so I cannot show you any basilisks here.

Figure 10.1. Drawing showing the positions of persons in a substitute photo of the basilisk.

Anyhow, I googled this picture, and sure enough, the search returned numerous images of people in work overalls forming the word ‘no’ with their bodies. It turns out this was in reaction to the 2017 Turkish referendum, in which Turkish president Recep Tayyip Erdogan asked for a massive expansion of his already-considerable powers. He wanted everyone to say yes to this deregulation, but some, like the person in figure 10.2, seemed to disagree.

Figure 10.2. Drawing showing the position of a person posing as a letter in a protest photo.

Heraldic Monster

In some cases monsters symbolise states. In fact, the city of Basel is symbolised by a basilisk. One of the best-known monsters in political theory is Thomas Hobbes’s Leviathan – which is not a basilisk but a sea monster, as well as Hobbes’s name for the state of the sovereign monarch.

Roko’s basilisk, however, is a specific monster, which is actually supposed to be a superintelligent artificial general intelligence (AGI). What kind of state does it stand for?

Perhaps it’s the ‘network state’ that Balaji Srinivasan advertises in his eponymous 2022 treatise.³ The network state is an entity that colonises physical space from the cloud. It first creates online crypto-based communities, which increasingly encroach on physical space via micronations, crowdfunded territories and sea-steading. A case in point is an entity called Praxis, which describes itself as the ‘next America built on the internet’:

Praxis is the world’s first Network State: a global online community with a national consciousness, developing a shared way of life, governing institutions, and crowdfunding a physical city. Praxians live in over 82 countries and have founded companies worth over $400B. Praxis is a home for the brave, who strive for virtue and wisdom. Our purpose is to restore Western Civilization and pursue our ultimate destiny of life among the stars.⁴

Praxis wants to crowdfund its own city in the Mediterranean, establish a ‘post-labour AI future’ and eventually relocate to space.⁵

Artificial Nature

A precursor to extraterrestrial community building can be found in the closed missions at Biosphere 2, 1991–1994. Here, volunteers were locked into a greenhouse sphere in Arizona for months on end to simulate an outer space colony. They had to be completely self-sustaining, including in the production of their food and a breathable atmosphere in a number of different biomes.

This phase of Biosphere 2 was an oligarch-funded test for space colonisation. Could colonists produce oxygen? Sustenance? Social bonds? The answer is – barely. Oxygen dropped to dangerous levels. The climate was completely fucked up. Most mammals went extinct, and pollinating insects were wiped out. The crew split into two hostile factions. After two runs and a scientist revolt, the project was abandoned. In the end, cockroaches and ants were the species that turned out to be best adapted to the oligarch space colony.

Why is this interesting? Because the manager of this project, in its later stages, was Steve Bannon, a key ethno-nationalist ally in US president Trump’s first administration. His management style – a sort of armed takeover of the premises because it wasn’t making profit – prompted an uprising. At a certain point, the windows of the sphere were forcibly opened by some renegade scientists. Ultimately, other windows were even broken to let oxygen inside.

Artificial Stupidity

But there is another interesting and much more potent detail: apparently Big Brother, arguably the first-ever reality TV show, was based on the Biosphere 2 experiments.

The narrative template of reality TV is based on an artificial idea of natural selection, whereby the fittest – however defined – survive or progress. One could also say that these formats are based not on selection but extinction, and that they celebrate extinction as spectacle. Interestingly, the Biosphere 2 inhabitants were in many cases linked to a hippie performance cult called the Theater of All Possibilities. On the level of theatre, if nowhere else, the Biosphere 2 experiments were spectacularly successful.

It is no exaggeration to say that reality TV has become an important template for autocratic politics – a development that began with Italian trash TV mogul Silvio Berlusconi’s rise to power in 1994 and has accelerated ever since. Just remember Donald Trump’s line in his elimination show The Apprentice: ‘You’re fired.’

With this in mind, it is clear that the results of Biosphere 2 had a spectacular impact on the development of cultural forms. Even if it had been perfectly sealed, reality TV, or the narrative of survivalist spectacle, would still have escaped. One can only wonder what kind of unexpected ‘thing’ or cultural effect will ‘escape’ from AI labs, and which unforeseen side effects this will have.

In fact, this has already happened on several levels.

Right now, AGI is still unrealised. But ‘poor’, lower-tech or hallucinating AIs are already busily restructuring society – more by dysfunction than by efficiency – and thus contributing to the rise of post-fascist, neofeudal and reactionary movements.

Why is artificial stupidity so successful? In the most rational of all worlds, bad and barely functional products are more profitable. Total optimisation is not reached when everything works well and people are happy; on the contrary, under the conditions of extreme capital, an optimal world is the one that barely scrapes by, deploying technology on the brink of failure. Ultimately, corruption, firing or jailing people, threats, rigging the system and pure deceit are way more efficient technologies. Under such conditions, one does not reduce emissions; one simply fakes the numbers and keeps selling cars. Or one simulates AI by employing human workers to mimic its functions.

Think of Roko’s basilisk, which doesn’t even try to convince you to support it. No, it just bullies and threatens you. The basilisk fable could be easily seen as a literary metaphor of little to no real-world consequence. A few people got very depressed reading, it seems, and Eliezer Yudkowsky, who ran the blog where the basilisk first appeared, freaked out and banned discussion about it for several years, citing information hazards. But while people were in distracted awe of the monster, a much more real development took place, more or less in parallel.⁶ On the same blog where the thought experiment had originally emerged, people began to brainstorm around the question how an AI would optimise its function. One of the unintended consequences may have been to serve as involuntary incubator for ‘alt-right’ ideas. As the post-humanist community forum RationalWiki assesses: ‘The (neoreactionary) subculture started amongst the Bay Area technolibertarian subculture, particularly including the transhumanists.’⁷

Discussants included many neoreactionary celebrities – Mencius Moldbug (aka Curtis Yarvin), Michael Anissimov and Nick Land – spanning ideologically from neomonarchist to fascist futurist. Such were the opinions expressed that the blog admin distanced himself repeatedly, and believably, from alt-right ideology. As theorist Florian Cramer analyses, Land advocates a sort of elite eugenicism, whereby rich people have access to racial upgrades.⁸ The wider neoreactionary ecosystem includes Peter Thiel, who famously claimed, among other things, that startups were akin to monarchies.

So, just as Big Brother escaped from the Biosphere and manifested both as eternal terror of reality TV and eternal mass surveillance, something else seems to have escaped from the sphere of Roko’s basilisk: namely, some of the cultural ideas of the alt-right and thus of the current right-wing US ideological elite. They believed that states should be corporations, that startups should be run like dictatorships (or the other way around), that so-called natural hierarchies should be protected and so on. Roko’s basilisk also became the ancestor to long-termist fears about extinction of humanity through AI. Long-termism – the idea that humanity’s actions now should be optimised to benefit its offspring a million years down the line – spawned large-scale scams such as so-called effective altruism associated with cryptofraudster Sam Bankman-Fried. Effective altruism – an ideology initially conceived to make philanthropy financially efficient – also heavily leans into eugenicism. Former Oxford scholar Nick Bostrom, one of its leading advocates, decried what was in his view a correlation between fertility and lack of intelligence, which he thought should be corrected.⁹

Long-termism is a leading ideology for many members of the current right-wing tech elite, and led to foregrounding future ‘existential risk’ caused by AI over its actual present-day social consequences. RationalWiki founder Eliezer Yudkowsky, still shaken from his encounter with Roko’s basilisk, became one of the leading voices to warn of AI-caused extinction:

Many researchers steeped in these issues, including myself, expect that the most likely result of building a superhumanly smart AI, under anything remotely like the current circumstances, is that literally everyone on Earth will die. Not as in ‘maybe possibly some remote chance’, but as in ‘that is the obvious thing that would happen’.¹⁰

He even advocates for airstrikes on data centres still training AI models after a wholesale ban. For sure, the Lovecraftian horror tale of the basilisk got Yudkowsky scared.

In parallel, however, the elaborate breeding fantasies of trans- and post-humanists were being realised in a low-tech way. In the wake of efforts to eliminate or diminish as many healthcare systems as possible, sick, marginalised and poor people have simply been left to die. This is a form of accelerated extinction, which is also implemented through border regimes and state violence. Another version of actually existing reactionary transhumanism takes the form of attacks on reproductive rights that aim to monopolise as much biopolitical control as possible.

To come back to the beginning: major reactionary pushes in many countries worldwide create multipolar surveillance authoritarianisms supported by AI systems. Simultaneously, AI is being heralded as a major technological and economical bonanza, and creates major hype.

All of these cause profound social disruption as well as the reorganisation of societies into polarised gangs fighting one another, or into self-declared micronations run by CEO sovereigns. An artificial state of nature prompts a surge of eugenicist necropolitics, flanked by awful memes. The heraldic creature of this mess is Roko’s basilisk, and it threatens to detain you. Which begs the question: What would you do if you happen to get locked up by Roko’s basilisk?

There are several possibilities.

First: let’s assume, for a moment, that Roko’s basilisk is a heraldic monster. So what kind of social contract does it represent? In fact, it represents the automation of social contracts and social relations as a whole. Both go hand in hand. What do I mean by this? An AGI – or, for that matter, a blockchain-based smart contract or DAO – promises to automate decision-making and replace it with a programmable function.

In the story of Roko’s basilisk, we see exactly how this works. You are promised some free will. You can support the development of this kind of AGI. (Yes or no?) But then, out of the blue, the basilisk starts to threaten and bully you, so you end up having no choice.

In a larger sense, this is precisely what whole societies have been hearing for decades: there is no choice but to accept the slow slide of the ‘no alternative’ model of neoliberalism into the openly autocratic rule of the basilisk. There is no choice as to how the benefits of automation are redistributed. There is no alternative to network capitalism.

So what to do now against the bullies and threats?

Suddenly I realised: Hold on. If Roko can travel into the future and bully people from his vantage point, maybe other people can, too. What if someone tried to send us a strong message from the future? Because maybe the thing that really exists in the future is not an autocratic, bullying basilisk but a commune or cooperative of red hackers who have finally realised a sustainable and fair economy of free access and redistribution of technological profits. And they are telling us very clearly, from that specific future, that if we want theirs to be the future, and not the basilisk’s, we need to do something right now. One might be bullied and threatened – and many already are being detained, and worse – but if anyone tells you that you have no choice, then you should say no (as we saw in fig. 10.1).

Come to think of it, a lot of people in workers’ uniforms have already been sent from the future, and they have all tried to tell us the same thing. We just weren’t quite smart enough to understand them at that time. Look, I unlocked the secret encrypted message in a photo of the Biosphere crew:

Figure 10.3. Drawing showing the positions of persons in a photo of the Biosphere crew.

The future singularity has been sending these messages for a very long time. And because we didn’t listen to them, they started escalating their appeals; now (as seen in fig. 10.4), they are sending in their most elite and senior representatives to make this point!

Of course, I am just kidding. I don’t believe that miraculous communards from the future are going to fix anything whatsoever. As you have already guessed, it was me, trying to alter the course of the future by changing my own slide presentation. And I am afraid, if you want to change the future, that you cannot rely on time travellers, either. Rather, you will have to actively change something in the present.

Figure 10.4. Drawing showing the positions of elderly persons spelling hayir in a protest photo.

Allow me to rephrase the question of Roko’s basilisk to reflect the new choice with which you are faced: You are presented with an entity that says you have no choice, and that if you don’t agree with him, he will intern you in a world with no alternative, forever, in which any decision is automated and determined by some sort of elite eugenicism, military-purpose AGI, cryptosovereigns and reality TV extinction spectacles.

Now decide. Of course, you are completely free!

In case you prefer to disagree, here is how:

11 Twenty-One Art Worlds: A Game Map

with Department of Decentralization and GPT-3

This text, an update to an existing template by Superstudio, forms the basis of a ‘choose your own adventure’–style game.¹ Players move across the map in a mixed-reality scenario, while figuring out how to navigate the fragmented, disconnected archipelago of post-pandemic art worlds. In this game, art-worlding is transformed into a world-building activity. As a first step, please decide how many of the below existing art worlds have become obsolete; then, at the end of this section, discover which type of art-worlder you are.

First Art World: The Carnivore Art World

The participants

Dream and die, eaten alive

Art world victims are

This art world consists of an excavating apparatus (a kind of turbine, with blades) that, in revolving, crushes rock, forcing all matter towards the centre of the cylinder and through a duct up to the ground. The art world happens when new contingents of participants are led underground using the winding screw as a pathway. The procession into the underworld is arranged at 6:00 p.m.; a choreography dictates a precise sequence of movements which participants have to execute, each imitating a small personal robot walking next to them. The traces they leave behind – slime, blood and other biological fluids – are photographed, censored and broadcast over a network as a QR code. Once the participants of the procession reach the bottom, they fall asleep and dream of an entirely different art world. They awake refreshed and then are eaten alive by the art world to sustain itself. An orifice 7.22 metres in diameter opens up temporarily on the level below participants. Their biological matter is digested by the machine while their dreams are marketed on some Discord channel. The carnivore art world is constantly creating new images and objects, and reproducing itself.

Second Art World: The Colosseum Art World

An island adorned

Public-shaming feasts

Art is a circus

This art world lies on an artificial island built by refugees from civilisational collapse. It is embedded in an urban system erected on poles, made of tree trunks from virgin forests. Its modules are adorned with elements obtained through robbery, plunder and forgery. On a quiet day, it basks in the sunlight of the sea artificially constrained by teleoperated modules of steel painted yellow. At its centre stands a dome ten meters in diameter and ninety metres in height, from which a putrifying odor emerges every two years. At this sign, participants start descending onto the art world. A feast is held for a period of many months. Its purpose is the play-testing of new modes of oppression. To this extent, participants crown temporal royalty and cancel all others. The public shaming and humiliation are recorded on the Solana blockchain and auctioned off. Famous people die in this art world: they come to study its rituals, but then the art world takes its revenge and kills them by eating their guts. As the public play-testing is taking place, the art world becomes a big circus with an outer ring and an inner one, connected by non-functional elevators and Ferris wheels.

Third Art World: The Museum of Modern Art Art World

Arcane maze MoMa

Shells and bureaucracy are

The art we consume

In the most disfigured, listless and numb area of that real estate that once was New York – more precisely, where MoMa once was at about Eighty-Third Street – this art world is incorporated as a cluster of shell entities. The structure is a 3D hologram with a length, width and height of fifty metres, consisting of ready-made junkspace cubicles, in each of which there is a lens twenty centimetres in diameter. This structure contains shell cubicles, which in turn contain cubicles in an infinite regress. Inside the cubicles, bureaucratic rituals take place under the pretext of child abuse. This activity sustains this art world, but actually, no one in the world understands its arcane, toxic and superstitious nature. The metabolism of this structure is performed by Picasso and Cézanne artworks circulating in between the shells, fueled by energised impunity. Systems of incorporated art advisories inserted in various points of the shell cluster enable them to communicate directly. At the centre of the art world is a shell. On the ground level, a series of gates lead to a labyrinth of rooms, corridors, staircases and basements. The building is completely sealed off from the outside reality. The whole building is one huge maze.

Fourth Art World: The P-Word Art World

Shame for the p

The p has been forgotten

Debt dissipated

This art world is built on the strict command never to articulate the forbidden word starting with p. This law has been existing for so long that everyone forgot what this word originally used to be. Every year, a growing number of people are executed under the suspicion to have harboured thoughts about the p-word. They are first ritually thrown into debt, symbolically eradicated by numbering their due in shame coins, and then have to confess. A conveyor belt grabs them and strips them of any remaining data. They can return now to the overground, but as a sort of virtual leper. In this world, special forms of madness are known. The most persistent plague is the roletarian fever, followed by the recarious rash. People who get affected are interned into huge quarantine facilities, shut in and forgotten about. This art world is based on strict laws, which are only made clear in case of an emergency. These laws are written in The O’word art world. In this world, every time someone utters the forbidden word, the art world itself splits into two.

Fifth Art World: The Booty Art World

Misminted, exiled

Vade retro auction houses

Players assemble

There are 815 million Nomads (NMS) and 13 million Exiles (EXS). The Exiles are an independent faction that can be only created in the world of the same name, which is the only planet in the system not controlled by a major auction house. The Exiles have a base at the furthest point from the system’s star, in a distant cold area of the system.

‘Exile’ is a common term for a person who has been forced to leave their homeland in order to escape legal prosecution or political oppression. In the case of ‘No Man’s Sky’, the Exiles are a faction of former NMS players who became separated from the main body of people who once played the game, and were forced into exile by being misminted.

There are 90 Types A and B, 15 Types X, 4 Types R, 1 Type H, R, X, and 4,817 Types W. For example, Type A provides 0.7 percent of all BOO sold in the World (in USDC), whereas Types W (for the region of Colonio LTX) provide a share of 0.1 percent of all BOO sold, plus 1 percent of all LIB redeemed (in USDC world credits).

If you are part of any private militia, you get an additional 500 voting weight.

The most popular weapon is the ‘Autocrat Expressionism’ canon, with usage of 2,466,321. The least popular is the ‘Zombie Formalism Offloaded’ pistol, with a usage of 59.

The most popular attack module is the Primitive Accumulation Blaster. If money, according to Augier ‘comes into the world with a congenital blood-stain on one cheek’, capital comes dripping from head to foot, from every pore, with blood and dirt.

An Exile can be seen briefly in the game’s intro video on the beach of the first planet the player visits, although it is unclear whether this is actually a live player character or an NPC character. ‘The floodgates of the privileged few have been opened, and the flood is just beginning.’

Sixth Art World: The Ville-Machine Habitée

This art world city is

Apparatus Maximus

Feeds from blood and lead

The art world is a machine, such a large machine that not even its inhabitants know its size. The participants are bureaucrats who live in the machine, endlessly dragged along by conveyor belts, chutes, and pneumatic tubes from the time of birth to the time of death. The machine takes care of everything, along the innumerable routes which intersect, unite and divide according to its incomprehensible programming and endless forms. The machine is self-sufficient; it takes from the outside world only the lifeblood of artists and a little lead. This triggers enough animosity for the art world to continue its drab and shabby existence, which would be missed by few people. The first thing that strikes us when we arrive in this art world is the silence. The silence is the result of the art world’s isolation, the absence of any contact with the outside world. The art world is isolated by a barrier of concrete, barbed wire and guard towers. This barrier is not a natural boundary; it was built by the art world itself, and it is maintained by the art world alone. The art world is self-sufficient, but its inhabitants have always known that its way of life is fragile, and that the slightest contact with the outside world would mean the end of the art world. The art world is fed by two pipelines, which emerge from the ground in the middle of the wall and stretch away into the distance.

Seventh Art World: War Art World

Military shows

Violence, supply, demand

Are in essence art

This art world consists of war shows. Battles and skirmishes are staged for the whole purpose of creating design objects, performative assets by means of generative destruction. The bones of the dead are studied by art world haruspices, auguring the advent of art prices and auction sales. War art world is the most recent of the art worlds. Its short history starts with the First World War and ends with the War on Terror. War art world is a state of mind. This art world is a hybrid world where the art world and war world overlap. One might say it is the ultimate artistic expression of military necessity. Art galleries are affected by the law of supply and demand and flooded by artworks. Their value is increased if they can be traced back to a conflict. Such institutions offer institutionalised violence for creating art objects. The origin of this art world type is based in ‘an objective mimetic standard of value for art’. In the course of history, the relation between carnage and art changed from a biological relationship – that is, when war is performed for the sake of survival of a certain group – to a causally artistic one – where violence is being staged for the purpose of art production. This form of art is called de-art since it is an art object which was produced due to a violent act.

Eighth Art World: Mutant Art World

Anarchy, lawlessness

Art is in a mutant form

No slaves of any debt

The eighth art world is wild and lawless, where artists and writers are embedded with cultural guerrillas who are setting up alternative communities which aim to be self-sufficient, beyond any form of governance or law. They want to be free from any form of government or financial debt, and they don’t want to be taken over by the corporate powers. They don’t mind being taken over by Boko Haram, however. Here ‘art’ takes the form of a mutant genetic algorithm that also functions as a means of communication in guerrilla cells.

Ninth Art World: The City of Art

Debt and loans in the cube

And artistic credit scores

Bankrupt very soon

This art world is embedded in the great cube. It is in fact an autonomous art cluster, but as it is part of the greater structure, it is both dependent on and independent from it. There are three parts to the city: first is the art market, where the artworks trade and commissions are agreed on; second is the museum district, where works are exhibited and sold; third is the residential district, where artists live and work. The artists, as long as they live in the city, will always be in debt to it. Artists who produce more works than required by the city will have their credit rating lowered, and will not be able to borrow money or exhibit in the city anymore. Artworks produced by artists that have been blacklisted by the city are considered worthless and cannot be traded anywhere else either. Artists can apply for loans from the central plan office in exchange for a percentage of future revenue on their works. Loans are paid back in ten years with an annual interest of 12 percent. If the artist can’t pay back the loan, the works produced with the loan will be auctioned off to the market and the money used to repay the loan. Every seven years, everyone goes bankrupt but the banks.

Tenth Art World: The Last Art World

At the end of time

Capitalism will fall down

Art goes extinct now

The tenth art world is the last art world. When the capitalist system collapses, there will be no more art worlds /fork too many artworlds. The art worlds will fade away into the dark ages. The artworlds will become extinct like dinosaurs and woolly mammoths. The art worlds will become fossils that are found in museums /fork they will be museums that are found in fossils. When capitalism collapses, people will no longer be able to live off the art worlds. They will no longer be able to make a living from their art. /fork people will finally be able to capitalise on collapse.

Eleventh Art World: The Animated Van Gogh Art World

An apparatus

Van Gogh copies ad nauseam

Chop an ear off here

This art world consists of an inflatable structure 10 metres wide by 200 metres long. In this structure lies enclosed an immersive scenario in which Van Gogh artworks are projected on every available surface. Under the inflatable structure lies a small torture chamber in which Xerox copies of Van Gogh paintings are tortured, by rotating them between acid tanks and electroshock machinery. The frequencies detected are then turned into 3D animations, sexed up with maximum Fresnel levels and sheen and forced into mobility. Meanwhile, the original paintings lie entombed in concrete rods, swaying with the rumble of earthquakes. In this artwork, you can pretend to be Van Gogh by cutting off replacement ears rendered in 3D. Additional wounds are optional. A good hole in the stomach will cost you only $300. The artwork is composed by a series of interconnected rooms, one of which has been designed to be a crime scene. The victim lies on the floor, and the bullet hole can be seen in the stomach. The bullet hole is an opening into a secret room, where the original painting lies. The visitors can cut off the ear of the original painting, and take it with them as proof of their visit to the art world.

Twelfth Art World: The VC Art World

Hedge and invest art

Sell and buy a token war

No exit ahead

This art world takes the form of a crypto-exclusive fund that fuses infrastructure, finance, art, unique collectibles and virtual estate. The fifteenth art world is decolonial de-fi. We are in the process of constructing a crypto-museum that is not only digital but also psychical. A place where our culture is preserved and protected. It will be a place where our culture is sold and bought. It will be a place where our culture is traded and transferred. It will be a place where our culture is stolen and returned. It will be a place where our culture is destroyed and returned. It will be a place where our culture is protected and destroyed. It will be a place where our culture is decentralised and stolen.

It is an art world that is not only highly ambitious but also extremely violent and deadly – so much so that it has been called a ‘war of tokens’, not to mention that it has been reported that there have been over 3,000 casualties and more than 11,000 arrests. The global South is the laboratory for the establishment of an art world whose goal is to be a permanent state of exception.

Disclaimer: This is not investment advice. I am not a qualified financial advisor. You should seek the advice of a professional financial advisor before investing in anything.

Fourteenth Art World: The Metamorphosis Art World

Locked up in MoMa

Living is a performance

A sentence for life

It is a month after the opening of the Metamorphosis exhibition, and ten artists have been recruited by the MoMA board to create a performance that rethinks the museum. The ten artists are:

–Alessandro Mendini, Italian designer

–Ben Vautier, French artist

–Caroline Achaintre, French architect

–Fabio Reinhart, Italian architect

–Pietro Ruffo, Italian artist

–Rirkrit Tiravanija, Thai artist

–Robert Rauschenberg, American artist

–Tomma Abts, German artist

–Ugo Rondinone, Swiss artist

–Yves Klein, French artist

Each of the ten artists is given a bedroom and is instructed to sleep in the museum for the month of the exhibition. They are to do whatever they like in their bedrooms, but they are not allowed to leave the museum. When they wake up, they are to make whatever they want in their bedrooms. Each day, the MoMA staff will bring them food and supplies. They are told that they will have to use the public spaces as well. The public spaces are to be used as studios. No visitors will be allowed into the museum at any time. The result is a strange metamorphosis. After a week of contemplation, Mendini opens his door and throws out a pile of garbage bags. Most of his ‘art’ is in those bags. He closes the door and never opens it again. Ruffo spends the first few days drawing. The drawings are amazing. He then gets bored and doesn’t do anything. Mendini’s garbage bags are dumped in the hallway outside his door. Tiravanija cooks and eats a lot of noodles in the middle of his room. The food is taken to him by MoMA staff, who just walk the food into his room. Ruffo is the only one to complain about this. Tiravanija is not allowed to leave his room, and is told that he can’t make any art while inside the museum. But he doesn’t care. Klein has filled the entirety of his room with balloons. There are no windows in Klein’s room, just balloons. After a few days, he lights a match and burns down the museum. Abts paints the walls of her room black and never leaves. She doesn’t even open the door. Reinhart makes a drawing that covers an entire wall of his room. It is absolutely gorgeous. It is a drawing of a drawing of a drawing. It is so beautiful that the museum is forced to buy it. Vautier makes one painting and then puts it up for auction. He has it sold within a few hours. Rauschenberg spends the first few days walking around the museum, examining everything. There are no windows in Rauschenberg’s room, just mirrors. After a few days, he appears in the lobby, in the middle of the day, and tells everyone that he has made art in his room and would like to leave. He is told that all ten artists have to finish their sentences. After a month, all ten artists are allowed to leave. The museum is shut down for repairs. They all receive a stipend.

Fifteenth Art World: The Democratic Art World

Expertise has vanished

All that remains is the art

Soulless and painless

This type of art world was created by extreme democratisation processes and artworks created by laymen without any practical experience. The democratic art world is an image of freedom and an occasion for individualistic self-expression. It is a culmination of the democratic society and is associated with ‘a consumable, transparent art world’ in order to generate bliss without pain, like a slide winding down a Frank Gehry atrium. The aseptic art world is dichotomised by experts and amateurs. It revolves around an absent guillotine. An optimistic concept of quality in art is created by this art world, which quickly becomes very popular. The democratic art world is giving birth to post-pandemic austerity as a cultural dominant, and art critics adapt to its dominant position in art. Under the surface, it ensnares real estate lots, messes up the rent, and triggers expropriation movements.

Sixteenth Art World: The Zoom Art World

This game is an Art

Come one, come all, join the Zoom

This Art is a game

De Groene Amsterdammer: ‘The workers in this Zoom performance are really ambitious. You can’t take that away from them. It is playful, which is always nice, but sometimes I can’t help but wonder whether the game is at the expense of the art.’ Focus Art Magazine: ‘I’m not sure whether the participants are to be congratulated or not. As employees of the Zoom art world, they have produced art for their bosses. But they have ended up working for nothing.’ De Volkskrant: ‘The Zoom art world is a great idea. Monumental, but also a little infantile.’ Robin Westerdijk from Wendingen, Nu.nl: ‘The Zoom art world is what happens when you cross the International Institute of Social Studies with a day care centre for senior citizens, which is a bit Schiphol, but also utopian. Until recently, van der Keuken was a project manager at the National Gallery in Amsterdam, and not so long ago he was an art student. “So here we are with petty-bourgeois ambitions.”’ Jasper Bakker from Volkskrant: ‘This is an art institution which is incapable of launching something. Some people just come here to paint; some others only do their laundry. In any case, the Zoom art world has been a great thing for van der Keuken. For the rest, it remains an unresolved experiment.’ De Groene Amsterdammer (again): ‘As it is, the Zoom art world is a solely local phenomenon. A sort of collective retirement home for old hippies. A place where you can have nice conversations over a glass of wine. But what happened to the revolution?’ Studio International magazine: ‘The Zoom art world, which has been in operation for over three years, now has to close its doors, not because it has run out of money, as was initially claimed, but because the Amsterdam authorities found that the structure is too unsafe.’

Seventeenth Art World: The Missing Art World

An art world amiss

Thirteen does not exist, no!

But Seventeen does

The Seventeenth Art World consists of the Thirteenth Art World, which was omitted.

Eighteenth Art World: The Token Art World

Stake it and fake it

Tokenise your phony loot

The rich are feasting

Behind a fake baroque facade, the eighteenth art world lies in hiding. One in a family of newly feudal art palaces, the eighteenth art world feeds on the sweat of stolen faces. Images of someone else are accumulated, headhunted, mauled and ground into sand to bake fake sandstone gargoyles as living mementoes to the fall of fake empires.

The token art world is an old feudal pastime. The token gesture takes the shape of a plaster cast of an ancient ruin, a manufactured da Vinci, a looted ethnographic artifact, a paternalistic gesture of cannibalism.

The token gesture takes the shape of a bronze cast of a child soldier, a manufactured Jackson Pollock, a looted African mask, a paternalistic gesture of cannibalism. The token gesture takes the shape of a fake performance by a fake artist, a fake performance by a fake charity, a fake charity performance by a fake charity, a fake charity performance by a fake artist.

Nineteenth Art World: The Fake Art World

Fund a circlejerk

The Whites Are At It Again

No substance remains

The nineteenth art world consists of the fake charity performance inside the eighteenth art world.

There is a moderator who introduces the guests: first the chairman of the foundation. The foundation does not want to change anything and praises the work of the museum as inclusive and multicultural. But the vice-chairman is sick of the colonial-loot discussion and wants to restitute and argues to open the institution for new voices. In the audience: a storage worker, who wants to keep things as they are as he needs to pay rent and is afraid of getting fired. One opportunist artist who pitches a critical project to get paid. A gallerist argues to put the artworks in question at auction. A group of activists that argues to exit the museum, take it over, or just bloody NFT the whole thing. Someone from a group that argues for the restitution of someone else’s ancestors’ ritual artifacts. Immediately new factions emerge. They include a white woman who argues the museum is a place of resistance. An academic argues the museum should be closed as it is a white-male-hegemonic-patriarchal-colonial institution. A security guard who wants to be a hero by stopping a protester. A woman who argues the museum should be a peripheral place, not the centre of Berlin. An Exile from ‘No Man’s Sky’ who wonders aloud what they are doing there. An administrator who argues the museum should not be under state control. A white person who asks why only white men can be racist. An Egyptian person who argues the museum should be in the Middle East. Someone who argues for the museum to become a gallery where artworks are destroyed in order to keep things new. A man who argues the museum should be a place to make Germany great again. A small personal robot that pitches investment into BOO coins. A white male artist who argues the museum is a place for him to be a star. A person who argues the museum should be a place for migrant workers as they cannot afford to go to the opera. A French bureaucrat talking about their experience at Administrators Anonymous. Someone argues the museum should be an institution of love. Another one wants to build an identical copy of the museum right next to it. Out of these people, two main opposing groups emerge. The first group is the ones that want to change things, and the second one is the ones that want to keep things as they are. The group that wants to change things argues that the museum is a place of death, that it is a place of violence where artworks are destroyed in order to keep them new and where people are being hurt and silenced. In the end, one of the main characters, the white-male-hegemonic-patriarchal-colonial institution, is shot by the security guard who wants to be a hero. Museumsgärten was written in German in 2018 by someone applying for funding impersonating Theaster Gates. It is based on the 2017 controversy of the ‘white cube’ of Berlin National Gallery. The work, set in an imagined art museum, was directed by the American independent filmmaker Bill Viola. It is filmed in the Tempodrom, a circular building in the former West Berlin, and was released on the video-on-demand platform Vimeo in 2018.

Twentieth Art World: The Fluid Art World

A submerged artworld

The one that will never be

Or be born at all

The Twentieth Art World got flooded before it even emerged. End of story.

Twenty-First Art World: The Next Art World

Why create weapons,

When one can create machines?

Nevermind, cancelled.

This is a variation of the Lucas Plan for the Art World. The Lucas Plan was created by arms workers who were threatened by redundancy in the 1970s. They came up with a catalog of socially useful products they could make with their knowledge and resources instead of creating weapons. What kind of products could the art world come up with?

The list starts with a machine to save the lives of small children who have been run over. It ends with a machine to make the people in power of the art world redundant. In between are (1) a machine to clean up the oceans; (2) a factory that recycles old art; (3) a machine that paints pictures that can’t be stolen; (4) a machine that uses words to make wars impossible; (5) a machine that doesn’t exist; (7) a machine that finds out why people give money to hideous rubbish in art; (8) a machine to cure people of resentment for being rich; (9) a machine that makes the people who decide now what art is a pile of worthless shit; (11) a machine that multiplies art schools; (12) a machine that tells when art is complete; (13) a machine that keeps on improving itself until it can’t be stopped; (14) a machine that makes art that cannot be read by any computer, which tells us what it is about; (15) a machine to find out what art is saying to the people who live near the art; (16) a machine that tells us how art really works; (19) a machine that removes all the ideas and makes it possible for anything to happen; (20) a biodegradable machine that makes art; (21) a machine that rids the world of the things that prevent people from being free (of aristocracy, of war, of money, of pain, of fear, of time); (22) a machine that disallows people to change their pasts; (23) a machine that makes everyone so radically different that all the words in all the languages have to be scrapped and new ones invented; (24) a machine that allows us to rediscover the earth and animals and plants in a new way; (25) a machine that allows people to believe in themselves; (26) a machine that allows people to know their place in the universe; (27) a machine that takes away the fear of dying; (28) a machine that rids us of the horrible idea that everything has to be explained; (29) a machine that allows the people who spend their lives trying to do something good to be allowed in and not gagged by the experts; (30) a machine to stop other machines.

The project ended in 2020 because it got canceled on social media.

Results of the Psychological Test

Of the art worlds whose descriptions you have read, how many are obsolete?

If you selected more than nine art worlds:

You are the head of an art fair, or hope to become one, or at any rate you are suited to be one. The logic and the mechanism of the system are perfected within you, and part of you, are you. You know the system, and it knows you. You are the system, and the system is you.

From six to nine art worlds:

You are an art critic. You don’t have to be a slave. You simply have to be a victim. Your victims are the artists. You are an element of the system, a cog functioning perfectly within its mechanism; lubricated by the logic of the culture, and thus free from friction, you turn smoothly, perfectly synchronised with your creator; hallucinating and sadistic, you generate terror. The system, however, is not the culture. The system is you.

From three to six:

You are a token. You have killed your doubts in order not to be killed by them, and you have died with them anyway. You don’t think, you don’t want; you only execute an order. Of the whole you, there remain but limbs and organs, nothing more than mechanical parts functioning in one direction only: from the assembly line to the junkyard. But there is still a chance for you to reinvent yourself, to free yourself from the slavery of your past (and its past). The orders are not the system. The system is you.

From one to three art worlds:

Are you a smart contract? Come forward to be gently sent to the zero address forever. Just use the hashtag #NFTnominate to nominate your peers. You can also tweet directly to us at @NFTcompost_nominate, or find us on the Telegram channel.

You didn’t think any of the art worlds was obsolete:

You are an artwork. You have not caught on: you haven’t understood that you are just a pretext. Is it possible that you didn’t realise that it is enough to carry forward the logic of the system until it becomes rigorous logic, to concretise many more hallucinating fantasies than those described here? Hold on; the way is broad; the ‘technologically advanced’ art worlds are running rapidly along it (ever nearer their goal), and the ‘developing art worlds’ are following close. The system is a system for the instrumental irrationality of art. And the most important thing for you to realise is that there isn’t any other system. The system is you.

Thank you to Matthias Planitzer

Acknowledgements

Thank you to Esme and Boris and to everyone who worked with me on this or anything else.

Notes

Introduction

1A word regarding the use of the common abbreviation ‘AI’ (for ‘artificial intelligence’): objectively, it is a total misnomer. More correctly, it should be called ‘linear regression plus back propagation’. I try to avoid calling it AI, however it is sometimes too cumbersome to always substitute this term with the the far more appropriate ‘machine learning–based tools’.

2Don Rassler, Muhammad al-’ Ubaydi and Vera Mironova, ‘The Islamic State’s Drone Documents: Management, Acquisition and DIY Tradecraft’, Combating Terrorism Centre, Westpoint, 31 January 2017, ctc.westpoint.edu.

3Ibid.

4Stephen Witt, ‘The Turkish Drone That Changed the Nature of Warefare’, New Yorker, 9 May 2022.

5Quoted in Joe Hernandez, ‘A Military Drone with a Mind of Its Own Was Used in Combat, U.N. Says’, NPR, 1 June 2021.

6Jussi Parikka, ‘What Is Not an Image? On AI, Data and Invisuality’, in Operational Images, University of Minnesota Press, 2023.

7Ibid.

8Svent Lutticken, ‘General Performance’, e-flux journal 31, January 2012.

9Bear in mind that, as with digital imagery in general, there are use cases which at least help conserve energy – for example, by avoiding air or car travel, or by avoiding other energy-intensive procedures such as renderings of computer-generated imagery.

10The probability for this is 0.000001 percent, calculated by my own proprietary bogus AI, which is just as legit as many other (bogus) proprietary AIs out there.

11Again, in some cases, use of these tools will prevent even greater wastage and CO₂ emissions.

12Reece Rogers, ‘AI’s Energy Demands Are Out of Control: Welcome to the Internet’s Hyper-consumption Era’, Wired, 11 July 2024; Isabel O’Brien, ‘Data Centers Emissions Probably 662% Higher Than Big Tech Claims. Can It Keep Up the Ruse?’, Guardian, 15 September 2024.

13Daron Acemoglu, ‘The Simple Macroeconomics of AI’, Economic Policy, 12 May 2024.

14See ‘The Hardest Part’ in is this volume.

1. ‘The Hardest Part’

1Will Douglas Heaven, ‘Generative AI Is Changing Everything. But What’s Left When the Hype Is Gone?’, MIT Technology Review, 16 December 2022.

2Ibid.

3Katie Kilkenny, ‘Tyler Perry Puts $800M Studio Expansion on Hold after Seeing OpenAI’s Sora: “Jobs Are Going to Be Lost”’, Hollywood Reporter, 22 February 2024.

4Brian Hiatt, ‘Washed Out Made an AI Music Video. The Backlash Was Swift’, Rolling Stone, 9 May 2024.

5Leopold Aschenbrenner, ‘Situational Awareness: The Decade Ahead’, Situational Awareness (blog), 2024, situational-awareness.ai.

6Future Fund, ftxfuturefund.org.cach3.com.

7For Aschenbrenner, AGI is more like an animated Excel assistant – nothing very sophisticated or capable.

8‘Leopold Aschenbrenner on Existential Risk, German Culture, Valedictorian Efficiency’, interview with Ben Yeoh, ThenDoBetter (podcast), 23 June 2021, thendobetter.com.

9Sebastian Sprenger, ‘Europeans Are Building a War Economy. Can They Master It?’, Defense News, 23 February 2024.

10Ed Ongweso, ‘Palentir’s CEO Finally Admits to Helping ICE Deport Ugandan Immigrants’, Vice, 24 January 2020.

11See, for example, Patrick Bernau and Carsten Knop, ‘Künstlerische Intelligenz’, Frankfurter Allgemeine, 18 April 2023.

12Bruno Maçães, ‘How Palantir Is Shaping the Future of Warfare’, Time, 10 July 2023.

13James Bamford, ‘American Company Fed Israel’s Killing Machine in Gaza’, Nation, 12 April 2024.

14Yuval Abraham, ‘“Lavender”: The AI Machine Directing Israel’s Bombing Spree in Gaza’, +972, 3 April 2024, 972mag.com.

15Ibid.

16‘Ukraine Develops Intelligence System Based on Artificial Intelligence’, Militarnyi, 20 October 2023.

17Paul Mozur and Adam Satariano, ‘AI Begins Ushering in an Age of Killer Robots’, New York Times, 7 February 2024.

18Mark Bergen, ‘Ukraine’s Vision of Robot Assassins Shows Need for Binding AI Rules’, Bloomberg, 10 June 2024.

19Dan Milmo, Alex Hern and Jillian Ambrose, ‘Can the Climate Survive the Insatiable Energy Demands of the AI Arms Race?’, Guardian, 4 July 2024.

20Ibid.

21Dan Milmo and Alex Hern, ‘AI Drive Brings Microsoft’s ‘Green Moonshot’ Down to Earth in West London’, Guardian, 29 June 2024.

2. Burnt-Out Images

1James Vincent, ‘Anyone Can Use This AI Art Generator – That’s the Risk’, Verge, 15 September 2022.

2Catherine Clifford, ‘Google CE: A.I. Is More Important than Fire or Electricity’, CNBC, 1 February 2018.

3Sam Altman, ‘Moore’s Law for Everything’, 16 March 2021, moores.samaltman.com.

4Bernard Stiegler, The Neganthropocene, Open Humanities Press, 2018, 40.

5Diffusion probabilistic models and their evolution are described in detail in Jascha Sohl-Dickstein et al., ‘Deep Unsupervised Learning Using Nonequilibrium Thermodynamics’, arXiv, 18 November 2015. There are also other unrelated necessary components to these systems such as image-text pairing models (CLIP is one example).

6See J. Rafid Saddiqui, ‘Diffusion Models Made Easy: Understanding the Basics of Denoising Diffusion Probabilistic Models,’ Towards Data Science, 2 May 2022.

7Nicole Starosielski, Media Hot and Cold, Duke University Press, 2022, 10.

8Jonathan Ho, Ajay Jain and Pieter Abbeel, ‘Denoising Diffusion Probabilistic Models’, NIPS ’20: Proceedings of the 34th International Conference on Neural Information Processing Systems 574, 6 December 2020, 6840–51. Thank you to Volker and Thomas Möllenhoff for debugging this technical part.

9Oliver C. Ibe, ‘Diffusion Processes’, in Markov Processes for Stochastic Modeling, 2nd ed., Elsevier, 2013, 295–327.

10Tom Goldstein (@tomgoldsteincs), ‘Diffusion models like #DALLE and #StableDiffusion are state of the art for image generation, yet our understanding of them is in its infancy. This thread introduces the basics of how diffusion models work, how we understand them, and why I think this understanding is broken’, X, 24 August 2022, x.com. Thanks to Thomas Möllenhoff for pointing out this example.

11Charles D. Joyner, ‘Black-Scholes Equation and Heat Equation’, Honors College thesis, Georgia Southern University, 2016, 548.

12‘To put this figure in context, the total value of all the products made by the world’s manufacturing industries, for the last thousand years, is about 100 trillion US dollars, adjusted for inflation. That’s one tenth of one year’s derivatives trading.’ Ian Stewart, In Pursuit of the Unknown, Basic Books, 2012, 297.

13Donald A. MacKenzie, An Engine, Not a Camera: How Financial Models Shape Markets, MIT Press, 2008, 11.

14Jonathan Beller, The World Computer: Derivative Conditions of Racial Capitalism, Duke University Press, 2021.

15William R. Morgan, ‘Finance Must Be Defended: Cybernetics, Neoliberalism and Environmental, Social, and Governance (ESG)’, Sustainability 15, 2023, 3707. Thank you to Baruch Gottlieb for pointing out this text.

16Ilia Shumailov et al., ‘The Curse of Recursion: Training on Generated Data Makes Models Forget’, arXiv, 14 April 2024.

17Alfonso Maruccia, ‘AI Feedback Loop Will Spell Death for Future Generative Models’, TechSpot, 14 June 2023.

18Ibid.

19@BasedBeffJezos and @bayeslord, ‘Notes on e/acc Principles and Tenets’, e/acc newsletter, 30 October 2022, effectiveaccelerationism.substack.com.

20Natalie Wolchover, ‘A New Physics Theory of Life’, Quanta, 22 January 2014.

21Émile P. Torres, ‘“Effective Accelerationism” and the Pursuit of Cosmic Utopia’, Truthdig, 14 December 2023.

22‘Effective accelerationism aims to follow the “will of the universe”: leaning into the thermodynamic bias towards futures with greater and smarter civilizations that are more effective at finding/ extracting free energy from the universe and converting it to utility at grander and grander scales.’ Ibid.

23Stiegler, Neganthropocene, 40.

24Starosielski, Media Hot and Cold, 1.

25Sean Michael Kerner, ‘Colonial Pipeline Hack Explained: Everything You Need to Know’, Tech Target, 26 April 2022, techtarget.com.

26Christopher Brito, ‘Officials Warn People Not to Fill Plastic Bags with Gasoline amid Panic over Gas Shortage’, CBS News, 14 May 2021.

27Kenrick Cai and Iain Martin, ‘How Stability AI’s Founder Tanked His Billion-Dollar Startup’, Forbes, 29 March 2024.

28Ibid.

3. Medium Hot

1Jen Kirby, ‘The Disturbing History of How Tear Gas Became the Weapon of Choice against Protestors’, Vox, 3 June 2020.

2Vincent Bevins, If We Burn: The Mass Protest Decade and the Missing Revolution, Public Affairs, 2023.

3Anton Jäger, ‘Political Instincts?’, Sidecar (blog), 19 March 2024, newleftreview.org.

4Ibid.

5See Nicole Starosielski, Media Hot and Cold, Duke University Press, 2022.

6Billy Perrigo, ‘150 African Workers for ChatGPT, TikTok and Facebook Vote to Unionize at Landmark Nairobi Meeting’, Time, 1 May 2023; Billy Perrigo, ‘Inside Facebook’s African Sweatshop’, Time, 17 February 2022.

7Benj Edwards, ‘Artists Stage Mass Protest against AI-Generated Artwork on ArtStation’, ArsTechnica, 15 December 2022.

8Jess Weatherbed, ‘ArtStation Removing Anti-AI Protest Artwork’, Verge, 23 December 2022.

9Theara Coleman, ‘Creatives Are Fighting Back against AI with Lawsuits’, Week, 26 July 2023.

10Daron Acemoglu and Simon Johnson, Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity, Public Affairs, 2023.

11See The Data Union, thedataunion.org; Blueprint: Governing Urban Data for the Public Interest, New Institute, October 2023.

12Bernard Stiegler, The Neganthropocene, Open Humanities Press, 2018, 41.

4. Knuckleporn or Phocomelia

1Ashley Belanger, ‘Child Sex Abuse Images Found in Database Training Image Generator, Report Says’, Ars Technica, 20 December 2023.

2At least this happened in an earlier release, in which porn was first excluded and then partly brought back again into the training data, after the resulting renderings failed to create anything ressembling human anatomy.

3Sharon Goldman, ‘I Interviewed the Founder of Civitai, an AI Platform Accused of Deepfakes and Porn. It Highlighted the Challenges of Open Source AI’, Sharon’s AI Extra (blog), 7 December 2023, sharongoldman.substack.com.

4James Bridle, New Dark Age: Technology and the End of the Future, Verso, 2018.

5Thank you to Volker Möllenhoff for pointing out this example.

6Carole Angier, ‘Cerium, How Chemistry Saved Levi’s Life in the Final Days of Auschwitz’, in The Complete Works of Primo Levi, vol. 2, trans. Ann Goldstein, Liveright, 2015.

7Hendrik Uhlendhal, Dominik Gross and Nico Biermanns, ‘The Ideological Roots of Nazi Eugenics in Pathology and Its Pioneers Martin Staemmler, Ludwig Aschoff, Robert Rössle, and Georg B. Gruber’, Pathology – Research and Practise 245, May 2023.

8James Linder Jones, ‘Thalidomide: The Real Story and the First Seal Baby’, HealthWorldNet, 16 November 2022.

9Ruth Blue, ‘Adapting to Life as a Thalidomide Sufferer’, Wellcome Collection, 6 May 2021, wellcomecollection.org. Children with impaired limbs were treated in clinics with experience dealing with amputees, such as Queen Mary’s Hospital, Roehampton. The hospital was renowned for rehabilitating soldiers over two world wars at its limb-fitting and amputee-rehabilitation centre.

10Ibid.

11Ibid.

5. Mean Images

1Ted Chiang, ‘ChatGPT Is a Blurry jpeg of the Web’, New Yorker, 9 February 2023.

2The Janus problem was initially identified by Ben Poole, a research scientist at the experimental Google Brain lab, and posted on his Twitter feed.

3See the website exposing.ai. A small sample of the names on the list included Ai Weiwei, Aram Bartholl, Astra Taylor, Bruce Schneier, Cory Doctorow, danah boyd, Edward Felten, Evgeny Morozov, Glenn Greenwald, Hito Steyerl, James Risen, Jeremy Scahill, Jill Magid, Jillian York, Jonathan Zitrain, Julie Brill, Kim Zetter, Laura Poitras, Luke DuBois, Michael Anti, Manal al-Sharif, Shoshanna Zuboff and Trevor Paglen. As Harvey and LaPlace write, Microsoft’s definition of ‘celebrity’ extended to journalists, activists and artists, many of whom were ‘vocal critics of the very technology Microsoft is using their name and biometric information to build’. Adam Harvey and Jules LaPlace, ‘MS-Celeb-1M (MS1M)’, exposing.ai, updated November 2021.

4Mei Wang et al., ‘Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network’, Computer Vision Foundation research paper, 2019.

5See also Allan Sekula’s seminal text on Galton’s composites, ‘The Body and the Archive’, October 39 (Winter 1986), 19.

6Wendy Hui Kyong Chun, Discriminating Data: Correlation, Neighborhoods and the New Politics of Recognition, MIT Press, 2021, 59; Lila Lee-Morrison, ‘Francis Galton and the Composite Portrait’, in Portraits of Automated Facial Recognition, Transcript Publishing, 2019, 85–100. Ronald Fisher, a founding figure in statistics, argued in ‘The Genetical Theory of Natural Selection’ (1930) that civilizations were at risk because people of ‘low genetic value’ were more fertile than people with so-called ‘high genetic value’ and recommended that caps be put on the fertility of lower classes. Fisher created the well-known Iris dataset, publishing the results in the Annals of Eugenics in 1936, to prove that one can discriminate between different species of irises using superficial measurements. Fisher’s flowers were proxies for a more sinister idea: if one could discriminate different floral species from superficial measurements, so too could one prove the existence of different races by measuring skulls. The Iris dataset is still taught to computer science students as a kind of foundational example.

7The Neyman-Pearson method, for example, encourages the construction of two hypotheses between which a statistical test can select, depending on the circumstances of the experiment. Like a profitable casino, ‘if the costs and risk are appropriately calculated, the house is bound to lose some hands, but over time it will ultimately win more’. See Justin Joque, Revolutionary Mathematics: Artificial Intelligence, Statistics and the Logic of Capitalism, Verso, 2022, 124–5.

8Ibid., 179.

9See Harvey and LaPlace, ‘MS-Celeb-1M’.

10Ana Swanson and Paul Mozur, ‘US Blacklists 28 Chinese Entities over Abuses in Xinjiang’, New York Times, 17 October 2019.

11Billy Perrigo, ‘OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic’, Time, 18 January 2023.

12Interview conducted by Tobias Timm for Die Zeit, Feuilleton Ausgabe no. 14 (30 March 2023) – a joint cultural supplement project on artificial intelligence by Marie Serah Ebcinoglu et al.

13Ibid.

14Interview conducted by Marie Serah Ebcinoglu for Die Zeit, Feuilleton Ausgabe no. 14 (30 March 2023) – a joint cultural supplement project on artificial intelligence by Marie Serah Ebcinoglu et al.

15Paola Tubaro, Antonio Casilli and Marion Coville, ‘The Trainer, the Verifier, the Imitator: Three Ways in Which Human Platform Workers Support Artificial Intelligence’, Big Data and Society 7, no. 1 (June 2020), 7.

16Chloe Xiang, ‘AI Isn’t Artificial or Intelligent’, Vice Motherboard, 6 December 2022.

17Tabitha Arnold, ‘Depression and Videogames’, Labour Intensive Art Substack, 6 January 2023.

18Phil Jones, Work without the Worker: Labour in the Age of Platform Capitalism, Verso, 2021, 50.

19Dwayne Monroe, ‘ChatGPT: Super Rentier’, Computational Impacts, 20 January 2023.

6. The Digital Rift

1See ‘How Much Content Is Uploaded to the Internet per Second?’, Spectralplex (blog), spectralplex.com.

2Jason Farago, ‘Why Culture Has Come to a Standstill’, New York Times Magazine, 10 October 2023.

3Kurt Andersen, ‘You Say You Want a Devolution?’, Vanity Fair, 7 December 2011.

4Reena Devi, ‘Will the Art World’s “Age of Average” Cost Us?’, Art Industry Insights (blog), 10 May 2024.

5Alex Murrell, ‘The Age of Average’, Alex Murrell (blog), 20 March 2023, alexmurrell.co.uk.

6Orit Halpern et al., ‘Surplus Data: An Introduction’, Critical Inquiry 48, no. 2 (2023).

7Tim O’Neill, ‘A.I. and the Age of Average’, Time under Tension, 24 March 2023, timeundertension.ai.

8Devi, ‘“Age of Average”’.

9Elizabeth Kolbert, ‘The Obscene Energy Demands of A.I.’, New Yorker, 9 March 2024.

10Brian Calvert, ‘AI Already Uses as Much Energy as a Small Country. It’s Only the Beginning’, Vox, 28 March 2024.

11Mehul Reuben Das, ‘Microsoft, Already One of the Biggest Polluters Because of AI, Saw Its 2023 Emissions Grow 30% from 2020’, Firstpost, 17 May 2024.

12‘AI Is Already Wreaking Havoc on Global Power Systems’, Bloomberg Technology, 21 June 2024.

13Leopold Aschenbrenner, ‘Situational Awareness: The Decade Ahead’, Situational Awareness (blog), 2024, situational-awareness.ai.

14He equally makes the case that current use cases are restricted to image generation and information processing as well as automated marketing: see Tej Parikh, ‘The Bear Case for AI’, Financial Times, 18 July 2024.

15Murrell, ‘The Age of Average’.

16Boaz Levin has made this point for photography in his excellent text ‘The Pencil of Cheap Nature: Towards an Environmental History of Photography’, Philosophy of Photography 14, no. 1 (January 2024).

17Kohei Saito, Karl Marx’s Ecosocialism: Capital, Nature, and the Unfinished Critique of Political Economy, Monthly Review Press, 2017.

18John Bellamy Foster, ‘Marx’s Theory of Metabolic Rift: Classical Foundations for Environmental Sociology’, American Journal of Sociology 105, no. 2 (September 1999), 380.

19Levin, ‘The Pencil of Cheap Nature’.

20Alfred Sohn-Rethel, ‘The Formal Characteristics of Second Nature’, Selva, 20 July 2019; Frank Engster and Phoebe V. Moore, ‘The Search for (Artificial) Intelligence, in Capitalism’, Capital and Class 44, no. 2 (June 2020).

21Sohn-Rethel, ‘Formal Characteristics’.

22Karl Marx, Theories of Surplus Value, part II, Progress Publishers, 1968, 24.

23As Esme Buden explains, water companies, for example, kept selling water, but refused to drain the waste water, leading to outbreaks of infectious diseases. Esme Buden, ‘Das Milljöh im Milieu, Soziale Wohnungshygiene am Beispiel der Berliner Cholera-und Tuberkulosebekämpfung Beitrag zum Geschichtswettbewerb des Bundespräsidenten “Mehr als ein Dach über dem Kopf. Wohnen hat Geschichte”’, 2022–23 (unpublished).

24Ibid.

25Devi, ‘“Age of Average”’.

7. We Didn’t Start the Fire

1Jon Laiseca, ‘Why European Clocks Are Running Six Minutes Late’, National Geographic, 13 March 2018.

2Anonymous cryptogear provider, interview by authors, 1 December 2021.

3Anonymous member of Communist Party, Kazakhstan, interview by authors, 15 February 2021.

4Chris Stokel-Walker, ‘The Generative AI Race Has a Dirty Secret’, Wired, 10 February 2023.

5World Bank Group, ‘World Bank Promotes Microwork Opportunties for Palestinians’, press release, 26 March 2013.

6Anonymous microworkers, North Iraq, interview by authors, 3 October 2023.

7John Malcolm Russell, The Final Sack of Nineveh, Yale University Press, 1998.

8Aka Zarkua, ‘Fire Devastates Abkhazia Main Art Gallery’, Eurasianet, 24 January 2024.

8. Who Does the World Belong To?

1Yann LeCun, ‘Objective-Driven AI’, presentation, University of Washington, 21 July 2023.

2Eileen Guo and Adi Renaldi, ‘Deception, Exploitation and Cash Handouts: How Worldcoin Recruited Its First Half a Million Test Users’, MIT Technology Review, 6 April 2022.

3Yann LeCun, ‘A Path Towards Autonomous Machine Intelligence’, openreview.net, 27 June 2022.

4Yejin Choi, ‘The Curious Case of Commonsense Intelligence’, Daedalus 151, no. 2 (Spring 2022).

5Quoted in Matthew Hutson, ‘Can Computers Learn Common Sense?’, New Yorker, 5 April 2022.

6David Marchese, ‘An AI Pioneer on What We Should Really Fear’, New York Times Magazine, 21 December 2022.

7Perttu Hämäläinen, Mikke Tavast and Anton Kunnari, ‘Evaluating Large-Language Models in Generating Synthetic HCI Research Data: A Case Study’, CHI ’23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, no. 433 (April 2023), 1–19.

8Ibid.

9Ibid.

10Ibid.

11Walter Benjamin, The Work of Art in the Age of Mechanical Reproduction, trans. J. A. Underwood, Penguin, 2008.

12Emily Soice et al., ‘Can Large Language Models Democratize Access to Dual-Use Biotechnology?’, arXiv, 6 June 2023.

9. Walk the Walk

1Ruth Catlow and Penny Rafferty, eds, Radical Friends: Decentralised Autonomous Organisations and the Arts, Torque Editions, 2022.

2A few examples: Adam J. Sulkowski, ‘The Tao of DAO: Hardcoding the Business Ethics on Blockchain’, Business and Finance Law Review 2, no. 2 (April 2020); Sterlin Lujan, ‘Curing the Disease of Control with Taoism and Crypto-anarchy’, Bitcoin.com News, 15 August 2018; Shawn Amick, ‘Bitcoin’s Stock to Universal Flow’, Bitcoin Magazine, 11 December 2021.

3David Graeber, ‘The Axial Age’, in Debt: The First 5,000 Years, Melville House, 2011, 224.

4On a much smaller and sillier scale, researcher Moxie Marlinspike has shown how NFT platforms easily manage to separate you from your assets, thus invalidating any idea of decentralised control of assets on the blockchain by exploiting poor UIs and access barriers. See Moxie Marlinspike, ‘My First Impression of Web3’, Moxie Marlinspike (blog), 7 January 2022, moxie.org.

10. Roko’s Basilisk

1This is a transcription of a speech delivered 25 March 2017 at Haus der Kulturen der Welt, Berlin.

2‘Roko’s Basilisk’, LessWrong (blog), lesswrong.com.

3Balaji Srinivasan, The Network State, thenetworkstate.com.

4‘About Praxis’, praxisnation.com.

5‘Praxis World Congress’, praxisnation.com.

6The example given was that of a machine trying to hoard paper clips by any means necessary. The question was, firstly, whether it would eventually start acting in ways harmful to humankind and, secondly, if it needed to be prevented from doing so. To which Nick Land replied that one could basically not stop an AI from optimising for its own gains and that it was a waste of time to try to programme any human goals into AIs. If it needed to collect paper clips, it simply wouldn’t care whether any humans were in the way. This might have been motivated by his idea that capitalism was already the pinnacle of a self-optimising system, and that there was not only no way but also no point in stopping it from eventually annihilating humankind. In this perspective, even survival had to fall by the wayside if it stood in the way of a machinic will to power.

7‘Neoreactionary Movement’, rationalwiki.org.

8FACTLiverpool, ‘Meme Wars: Internet Culture and the “Alt Right” | Florian Cramer Talk at FACT’, youtube.com, 7 March 2017.

9Nick Bostrom, ‘Existential Risks: Analyzing Human Extinction Scenarios and Related Hazards’, Section 5.3, nickbostrom.com.

10Eliezer Yudkowsky, ‘Pausing AI Developments Isn’t Enough. We Need to Shut It All Down’, Time, 29 March 2023.

11. Twenty-One Art Worlds

1Superstudio (Gian Piero Frassinelli), Twelve Cautionary Tales for Christmas: Premonitions of the Mystical Rebirth of Urbanism, 1971, available at issuu.com.