‌1

Introduction

Mél Hogan, Stefan Laser and Edward Ongweso Jr.

Through Predictions, we sculpt the possible into being. The act, or gesture, of predicting is performative. Theorists, scientists and technologists alike usually predict to forecast the future publicly, hoping to be rewarded for having known what was coming. There’s pleasure and pride – and perhaps an emboldened sense of expertise – in having correctly ascertained the future, even if the emerging reality you anticipated turns out to be dire, grim and dreadful. To know that bad things await us gives us a (sometimes false) sense of preparedness. If we (predictors) can tell what’s coming, we tell ourselves that it’s because we’ve factored in the right variables or detected patterns – like predicting rainfall at a precise time and place, or – of greater consequence – predicting an asteroid hitting Earth, the next pandemic or the demise of democracy. Underlying the urge to make predictions, however, is an admission of our inability to ever really know what’s to come, despite a deep desire to do so, and perhaps to control something about the future. As we predict, we usually reveal our own drives, beliefs, ideologies and biases. This is why it’s also possible to make technologies of prediction that don’t predict but give that illusion – and work to coerce and manipulate those they gather data from and survey. Corporations are making massive investments in machines that claim to predict, that sift through past data to make sense of the future. Thus, prediction becomes an ideological instantiation – a feeling made into an infrastructure that legitimises itself as a scientific quest.

Across the three volumes, we try to use Predictions to our advantage, turning to a format that we are not used to in our writing. We thus join John Urry (2016) in calling on the social sciences and humanities to engage more, and more consciously, with future-building. We invite you to engage with Predictions not as fixed endpoints, but as openings – opportunities to read generously and wander thoughtfully. To let go of extractivist habits of reading; to resist the pull to mine for definitive conclusions. Instead, we invite you to trace the threads that resonate with you, even if they fray at the edges. There is a rhythm to our selection of pieces, a coherence we felt, a thought process behind the order of things. We will elaborate on that later. But the path you take is yours to make. Worlds are to be built. the sequence of entries means little. Read against the grain, with rigour shaped differently, allowing the texts to breathe. We remind each other to breathe.

Coming to terms with a troubling term

Predictions are claims about the future that are meant to come true. To predict is to say or estimate that something will happen in the future, or will be a consequence of some action(s) or set of circumstances – but it is inherently neither a promise nor a prophecy. A prediction is a statement about a future event based on data and analysis, while a promise is a personal commitment to undertake something, and a prophecy foretells future events, often with divine or supernatural implications. Kristen B. Hastrup (2007) argues that prophecy, like prediction, is a form of knowledge about the future – but prophecy is not about calculating outcomes based on evidence (like in scientific prediction). Instead, prophecy is about making claims that shape the future by calling people to action. In other words, prophecy isn’t just about seeing what will happen – it can mobilise people and influence what comes to pass. In this sense, predictions are speculative and reflect the logics of our current computational era, where the ability to analyse data is the closest we can get to understanding patterns from the past that reveal certainties in the future. As Sun-ha Hong (2020) explains it, ‘Technologies of datafication renew the long modern promise of turning bodies into facts. They seek to take human intentions, emotions, and behavior and to turn these messy realities into discrete and stable truths.’ The knowledge and truth produced may serve the police state and workplace surveillance; that is, particular futures are thus made more actionable than others (Benbouzid 2019; Amoore 2020, 160; Hong 2023; Lazaro 2023; Egbert/Heimstädt 2024). Prediction becomes control that fears the uncertain.

Predictions reflect our worries and aspirations back to us. How actors make predictions varies. These days, predictions are often made using statistics, and increasingly are based on large datasets, using machine learning or large language models. The broader public got used to this large-scale predictive mode because of the Covid-19 pandemic and live dashboard updates (Naudé/Vinuesa 2021). Another well-known and thoroughly theorised prediction is Moore’s Law: the prediction that the size of resistor gates will decrease, thus increasing computing power and efficiency over time (Pasek 2024). As Lente and Rip (1998) put it, Moore’s Law became a self-fulfilling prophecy, with industry actors using the prediction as a guide for investment and finding new ways to solve or trick the calculative task at hand (less is more). Without access to high-throughput infrastructure, predictions might be done by hand, by systematically observing and documenting a phenomenon in order to anticipate possible outcomes, or be based on prior experiences, narratives, hunches or telepathy (Dickens 2025). However, not all predictions are quantitative or industrial. Notably, there are conceptual outlooks that try to tame today’s public issues by anticipating future developments. In this sense, researchers in Science and Technology Studies (STS) have integrated discussions of predictions into their work in various ways and for various reasons: to analyse sociotechnical imaginaries in which predictions describe desirable futures (Jasanoff 2006); to analyse models in financial markets (MacKenzie 2023); to analyse and partly advise on governance and policy – also framed as anticipatory expertise (Felt 2016), in order to align risks and regulations (Knowles 2013); and to deal with foresight and technical assessment studies (Bechtold et al. 2017).

Among the many domains where predictive practices shape collective decision-making, climate change discourse stands out as particularly reliant on future-oriented reasoning (Barnett 2025; Davidson 2024; Lake et al. 2024). We could call this collective anticipation, as Masco proposes, ‘merging prediction with expectation and fusing fear with desire for alternate outcomes’ (2020, 36). Think of Mann’s and Wainwright’s (2018) reflection on future climate governance – which might evolve, as the authors suggest, into a Climate Leviathan, Climate Mao, Climate Behemoth or something else entirely, depending on the future of democracies and capitalisms worldwide. In fact, we know the climate future only in the multiple, through the various models and political calls for change (Omura et al. 2021). And this modelling includes drama. An emerging eerie Anthropocene genre paints future worlds that live through the worst that is yet to come for today’s inhabitants of planet Earth: mass extinction (David Wallace-Wells 2019). While such imaginings may defy prediction through data – big and small – conceptually enriched stories help navigate unpredictability.

Predicting often involves a certain arrogance or hubris – an assumption that we are able to foresee future events, to know how things will unfold. It is presumptuous to think we can glimpse the destiny or trajectory of complex systems and societies. The act of prediction implies that we can overcome the inherent uncertainties of the future by the power of our intellect and the patterns we believe we have discerned. It is a social entanglement, resting on institutions. So making predictions – often publicly – can reflect an arrogant self-assurance that we have gained special insight into forces beyond our control. Laura Watts, one of the authors in our volume, reminds us of the competing interpretations of hubris in STS-related fields (Watts 2018, 294f.). There are authors, such as Jasanoff (2003), who accuse tech designers and governance makers of deliberate blindness to the ambiguities of predictions. And then there is the Sociology of Expectation (e.g., Brown and Michael 2003) that reads predictions as pragmatic instruments, a constructive and necessary part of technology in development. The future often humbles those of us who think we know what’s coming, yet turning to the becoming is a way to move ahead (Sadowski 2025). This is how we move from hype to hope.

We make Predictions as a gesture of hope and optimism that goes against the current momentums that trouble us. As Manu Luksch (2021) phrased it, ‘Prediction is the uncanny sister of hope’ because predictions are not simply forecasts of an inevitable future but openings for imagination, negotiation and intervention. Predictions make visible the built-ness of the future, inviting collective critique and the possibility of reshaping what is to come. Predictions like to surprise, while at the same time being surprisingly open to forgetting many of the things that shape our current and past life. Remembering, inventing, surprising are all political, just like forgetting (Nyssa 2020). We thus use Predictions as a move to re-emerge; a playful proposal or a guide about what’s not yet in play, but could be.

Handling predictions in science and technology

Still, as critical scholars and practitioners, and as the editors of this series, we have felt uneasy with the dominant, rigid ideals of predictive technologies that are sold to the public as innovative, practical and objective. This is similar to editors of the Practising Comparison volume grappling with a method and its troubled history (Deville et al. 2016). A number of critical observations converge in our feeling of unease, from general enlightenment and a questioning of Big Tech hype, to issues of power, domination and environmental extraction. For this reason, in this section we have a closer look at the critical STS literature on predictions and work out conceptual nuances and historical specificities.

Generally speaking, it is worth reminding each other that it is not actually possible to predict, to know with certainty that a certain event will play out in a specific way. Moreover, predictions often rest on models, assumptions or data that may be incomplete, biased or dubious. This raises questions about who shapes predictions and whose interests any given statement serves.

In this context, STS scholars interrogate the epistemic foundations of predictive practices, questioning not only their reliability but also the implicit assumptions that may privilege certain actors while sidelining others. Predictions, rather than being neutral forecasts, can function as tools of power – potentially reinforcing dominant perspectives and silencing critique (Jasanoff 2006). Richard Tutton’s (2020) exploration of sociotechnical imaginaries reveals how utopian visions – such as Silicon Valley’s aspirations for multiplanetary colonisation – serve as speculative futures and performative frameworks that shape present-day technological investments and more-than-social expectations. Lucy Suchman critically examines the allure of newness in predictive technologies, for example recently in military operations (Suchman 2025). Her work emphasises how the presumed objectivity of technoscientific systems can mask the underlying assumptions and erasures inherent in their design and deployment. With the rise of digital capitalism and its capital-intensive lead firms, the last two decades have seen a turn away from scientific applications of predicting towards business decisions that are made in the face of uncertainty and with one key goal: cheap productivity. Cycles of hype around big tech reveal investors in search of profits and rentiership (Birch 2019). This generates an addiction to prediction (Weatherby and Recht 2024) that only a few benefit from. The most recent hype, so-called ‘AI’, has been able to secure a new round of investment in this way. Nations take up promises from business sectors, investing hundreds of billions of dollars in an AI infrastructure of constant prediction. (Bareis and Katzenbach 2021).

Generative AI is an interesting example for thinking through the troubles of predictions. Chatbots, foremost, produce nothing but probability-based predictions of word sequences. They try to perform in confidence. A chatbot draws on the past and (increasingly) near-real-time decisions, on big data sets from yesteryear to today (Aradau and Blanke 2016), and it sets out to establish a definite future outcome, to intervene in time and space. When you submit a prompt, the AI anticipates what the next appropriate word might be. As is well-known, however, this application of AI often eerily misses how reality actually looks and feels (Doctorow 2024); hallucinations are taking over. Some things are just ever so slightly off, and it makes you wonder what’s missing.

But critical researchers worry about more than data extraction for the sake of shareholder satisfaction and off-putting output. Speaking through planetary entanglements, predictions produced by generative AI are fuelled by a vast material machine. Energy demands are deeply entangled with the infrastructural and extractive imperatives of digital economies, and with AI in particular, as high-tech industries rely on an uninterrupted flow of electricity to sustain data centres, production facilities and computational processes (Hogan 2018; Crawford 2021; Rella 2023). The manufacturing of microchips, built with (rare earth) minerals and hazardous chemicals, exemplifies the ecological contradictions, where the material residues of so-called immaterial economies – waste, toxicity, and environmental degradation – are systematically displaced to the peripheries of global supply chains (Herod et al. 2013; Ensmenger 2018; Lepawsky 2022; Greeson et al. 2020). Furthermore, the vast water consumption required for semiconductor fabrication, data centres and their cooling systems exacerbates regional water crises, while the carbon-intensive logistics of transporting these components drive emissions and accelerate climate change, underscoring the infrastructural violence embedded in digital expansion (Carr et al. 2020; Edwards et al. 2024).

There are powerful and useful critiques of these developments, yet criticising hype cycles can feed an affirmative type of critique, what Vinsel (2021) calls ‘criti-hype’ and Burgess (2023) flags as ‘Big Critique.’ This kind of critique also impacts some STS research, where AI criticism becomes a self-assuring gesture, a justification to hunt grants. Although there is some criti-hype in academia, techbros remain the primary culprits. This is particularly problematic in the context of public controversies around AI (Marres et al. 2025). Recent research shows how the public AI discourse is shaped by sceptical narratives that, however, are critical only on the surface and mostly function to ‘flood the zone’ with ever more speculative information. Hence, the actual matters of concern remain concealed. We would like to point out that academia is integrated into structural growth imperatives, which might motivate academics to take part in public criti-hype cycles. The more we collectively buy into the idea that data and technology hold absolute insights for better managing humanity, the more we surrender to the idea that we can, with the proper tools and concepts, really see what’s coming and mitigate the damage with tools and fixes. And the less we turn to art, ideals, concepts and creativity to problem-solve and embrace realities.

Still, there are many ways to predict; in predicting, we sow the seeds of an alternative reality. To get a sense of alternative performances of prediction, we can build on the ethnographic, practice-oriented lens of STS and Future Studies (e.g., Sardar 2010; Urry 2016; Andersson 2018). Scholars emphasise that predictions are an intrinsic, valuable, often successful part of the natural and technical sciences, mediated by mathematical models. Maiers (2018), moreover, makes the case for cherishing predictions explicitly made by humans, highlighting the evaluative backdrop and unique sensitivity of qualitative, ethnographic prediction. Attuning to cases, sites, actors and their experiences works out. And it is crucial to not just assume the performativity of predictions (Leawell 2020) but investigate the ifs, hows and whats. In general, there are different approaches to prediction in science and technology that do not necessarily reaffirm rigid power structures.

We will briefly trace the technoscientific history and practice of predictions and then turn to sf (science fiction/speculative fabulation), to bring out the generative power of predictions, a precarious, uneasy, but liberating form of prediction that our authors have approached in heterogeneous forms. We invite you, as a reader, to fray out the trouble (Haraway 2016); to keep the concept of prediction generously open, to rip it and to stretch it in different directions.

Science studies, which examine how scientific knowledge is produced, along with a close analysis of how different cultures understand objectivity and predictions, help us maintain a critical perspective. Objectivity is not a fixed or external truth but something that scientists actively create through their work (e.g., Latour and Woolgar 1986; Knorr-Cetina 1999). Objectivity has not always been seen as an absolute ideal. But its meaning and practice have changed over time, reflecting different historical situations (Daston and Galison 2007). Feminist theory, particularly standpoint theory, has moreover challenged the notion of objectivity by emphasising that knowledge is always shaped by the actor performing science. Haraway (1988) famously criticised the idea of a ‘god trick,’ which assumes an all-seeing, seemingly neutral perspective, and instead argued for ‘situated knowledge’ – the idea that all knowledge is created through specific practices in concrete sites of action. From this angle, the way we develop and use predictions in science can be understood as part of the historical shifts in what counts and works as objective knowledge.

Johnson and Lenhard (2024) identify four distinct cultures of prediction, each emerging in different historical periods, shaped by specific tools and infrastructures, and often merging in practice. The first, a rational culture, rooted in the philosophy of science, draws on logical deduction and mathematical models to make predictions. The second prioritises observational and statistical methods – the authors coin it an ‘empirical culture.’ This approach was dominant before the mid-twentieth century, often incorporating dispersed user input. We have seen it in nineteenth-century Dutch weather forecasting, for example, through the wisdom and practical knowledge of sailors (Baneke 2025). However, contemporary predictive systems increasingly sideline individual expertise. We can identify a shift with the rise of centralised computing (starting with mainframes), giving rise to an iterative-numerical culture. Here, predictions are refined through computer simulations and data analysis. Finally, with personal computing, an exploratory-iterative culture emerged. It features both empirical and numerical approaches to cast adaptive models. This is the basis of modern forecasting methods, particularly Bayesian statistics, which continually update probabilities based on new evidence. The increasing reliance on model-based prediction is controversial, though. As Johnson and Lenhard (2024, 186) note, the focus on tuneable parameters to refine predictions often comes at the expense of deeper explanatory understanding. This is how the hallucinations of a generative AI model come into being.

In the summer of 2024, the Western media fixated on the tragic yet absurd fate of a billionaire’s yacht, the ironically named Bayesian. The opulent vessel sank, taking the lives of dozens of passengers, when it proved ill-equipped to withstand the fury of a storm on the open sea. But the tragic loss of life was overshadowed by the absurdity of the disproportionate attention lavished on the plight of the ultra-wealthy while refugee boats, sinking in the same seas, went largely unnoticed. The fact that the name of the ship was Bayesian – a name synonymous with precision through prediction and calculation – and that it faltered so disastrously when it mattered most, only added another layer of bitter irony to the story. Meteorologists bear witness to the fact that predictions cannot save you from the notoriously difficult-to-predict weather (Fine 2010). Sailors used to know this (Baneke 2025). Developments overlap and intertwine, and this probably also applies to the dominance of the explorative-iterative culture. Johnson and Lenhard (2024) take current developments in the software and AI world as an opportunity to outline the emergence of a fifth culture; a so-called ‘pure prediction’ based on deep learning architectures, with the help of readily available software. More and more predictive models roll out on new centralised cloud systems, virtualised on widely distributed hardware around the world, launching calculations without in-depth understanding and using those calculations as the basis for decision-making.

Here is the key. The question is whether a prediction ‘threatens to further close a future that should remain open or rather helps to close a future – hence making it predictable – that is dangerously open.’ (Johnson and Lenhard, 2024, 132) That’s why it is crucial to have a rich tapestry of predictive thinking at our disposal; a diversity of approaches that makes explicit how to open or close alternative realities. We don’t know what is to come, and this is good at least, it is good to know. It is crucial to study, as Liliana Doganova put it, ‘when and how uncertainty is mobilized and by whom, what form it takes, and what effects it produces.’’ (Doganova 2024, 171) Remember Moore’s law and what Lente and Rip (1998) called a self-fulfilling prophecy: ‘We may speak of a self-fulfilling prophecy,’ the authors reason, ‘but the fulfilling did not occur because it was a prophecy, but because actors took up the prophecy and acted accordingly.’ (Lente and Rip, 207) The example of Moore’s law works well because the actors involved have a reasonable understanding of their connections in the value chain and the competition. This means that how exactly uncertainty is mobilised varies and is, to a certain degree, up for debate. We need to work against stable ontologies, as Haraway (2016) urges us to do, against the inevitable, reimagining the possible. Let’s act accordingly.

sf-ing the predictive gaze

Apart from the sciences, predictions have been prominent in sf, aka science fiction or scientific fabulation. For Future Studies, sf serves as a bridge between the imaginative, the analytical and the speculative (Gumbs 2018). Without this bridge, predictions miss critical infrastructure to build on. We, in this volume, stay close to data infrastructures and their histories/futures, and hold that sf predictions are indeed an important part of internet culture. Wikipedia has a ‘list of existing technologies predicted in science fiction’, from space rockets to video ads in taxis. The Sci-Fi fandom Wiki features a ‘timeline of fictional future events’, alternative futures that turned into reality or so-called failed predictions, while social media platforms and Google queries are constantly fed with ‘Did {sf x} predict {technology y}’. Readers apparently want to believe in the geniuses of the literary scene and declare them saints by scrolling through past work. Others scroll forward. Some entrepreneurs claim to have tackled projects or companies inspired by sf. Think Silicon Valley and Burning Man (Turner 2006).

sf authors themselves, indeed, have an ambiguous relation to the act of predicting and public expectations around getting the future right, for two reasons. First, sf has rarely anticipated future trends or technologies correctly, save for a few outliers that are often noted in public discussions. The online Encyclopaedia of Science Fiction thus begins its entry on prediction by saying: ‘The most widespread false belief about sf among the general public is that it is a literature of prediction’. In fact, we learn, sf almost missed out on noticing the rise of the internet, preoccupied instead with the ascent of robots. In a way, robots have indeed arisen as our overlords, but not in the way we – or at least, sf creators – expected.

Second, for plenty of sf authors, being reduced to predictions feels restricting. For Isaac Asimov (1981), the notion of prediction sounds like a trivialisation of sf. An insult almost. This has become obvious, considering all the circulating one-dimensional readings of science fiction. Think thief Elon Reeve Musk. Ursula K. Le Guin (2000, 8) finds an elegant comparison. ‘Predictions are uttered by prophets (free of charge), by clairvoyants (who usually charge a fee, and are therefore more honoured in their day than prophets), and by futurologists (salaried)’. Then, she adds, ‘Prediction is the business of prophets, clairvoyants, and futurologists. It is not the business of novelists. A novelist’s business is lying’. This is an interesting critical twist. What kind of lies are we talking about, and where are they heading? Cory Doctorow (2012) has an idea: ‘Science fiction writers are pretty useless as fortune-tellers, but who needs fortune-tellers?,’ he asks. ‘‘‘Prediction’ implies a future that we hurtle towards on rails, prisoners of destiny. Having a route-map for the railroad is nice, but wouldn’t it be better if we could steer?’ That sounds liberating and quite straightforward, but what is most important is that different sf authors choose different colourations of ‘lying’ and ‘steering’ that can take the form, we would argue, of different prediction rhetorics. We acknowledge the distancing – our authors have noted similar doubts – but we hold on to the power of the term, and run with it. The spectrum of prediction used in sf ranges from a belief in current advances in the sciences to claiming the past and future through critical retellings.

sf is born out of advances in science and technology and can be defined as the appreciation and exploration of scientific learnings. sf works with plausibility. The sub-genre of hard sci-fi virtually clings on to technical and scientific facts, becomes captivated by them and mobilises predictions. A classic example is Rendezvous with Rama by Arthur C. Clarke, which predicts human access to the solar system, with colonised planets and asteroids serving as a base for exploration. In other words, the author plays with the empirical, experimental predictive culture outlined by Johnson and Lenhard (2024). What is interesting for us in this case is how predictions make it possible to value human capacities.

Predictions have a different use in utopias, dystopias, novels about alternative histories and futures. To explore themes, authors very often pose the question: What is plausible anyway? Plausible for whom? Here, social imbalances are extrapolated and predictions used to rethink the current state of things, emphasising that it could and should be otherwise. Think of The Man in the High Castle by Philip K. Dick, Patternmaster by Octavia Butler, through to Pauline Gumbs’ M Archive, where philosophies and schools of thought have been born and torn. Cyberpunk is a special case that stands out in the face of the internet and Big Data, topical themes which we have put centre stage. Neuromancer by William Gibson, in turn, depicts near-future dystopias dominated by advanced technology and cybernetics, critiquing corporate control. Cyberpunk, we are tempted to add, however, can be annoying. Its criticism feels, well, predictable.

The tone of cyberpunk is often gritty, cynical, whereas (for example) Solarpunk and Climate Sci-Fi try to cherish the planetary entanglements and alterity that makes life on Earth (and other potential planets) possible, and cherishable at times. Extensive oeuvres such as those of Kim Stanley Robinson or Nnedi Okorafor are famous for these experiments, where futures of living with climate disaster and the capacities of modern technology merge. The sf accounts deal intensively with the perspective of users, maintainers and other forms of expertise. One could say as a counter-design to the dominant cultures of prediction, as Johnson and Lenard (2024) put it, which have moved away from the user perspective in the last century. An interesting example in this regard is offered by Sue Burke and the Semiosis novel. Peace and thriving, we learn, is only possible through close collaboration between humans and other living (and nonliving) beings. Recent findings from the field of botanics (in short, plants are clever) are predicted to be particularly applicable to the future, and readers can physically feel the power of knowledge in action. Here, we are reminded of a classic-in-the-making of STS, Arts of Living on a Damaged Planet (Tsing et al. 2017), where the assembled authors pursue the ghosts and monsters of the Anthropocene and explore life in hyper-capitalistically charged infrastructures. We hope to cope, and live.

Postmodern technoscience brings forward a very forceful, in this sense, relatable, engaging sf. This, first, is a nod toward an STS classic of sf: Donna Haraway’s Cyborg Manifesto – born out of and set against Cold War bellicosity.. Conceiving this manifesto then motivated Haraway to develop the alternative frameworks of multispecies conviviality, as unfolded in The Companion Species Manifesto and Staying with the Trouble. If we want to and must take symbiosis seriously, then the prediction is that multispecies living and thriving will follow. A related, again forceful, sf project is The World After Amazon (https://afteramazon.world/). The project gives 13 Amazon workers a space to express themselves. They remind us of a savvy surveillance regime in which predictions face predicaments. And even more, this work reminds us of the purpose of infrastructures, perhaps the purpose of our (more-than) academic infrastructures, too. How can we help create an academic space that works for the public good through our imaginations and the material infrastructures we build to make predictions fly?

All these literary explorations reveal a diversity for future-making through prediction. It works if it serves as worlding and then can go in many directions. This helps enrich (and expand on) what Maiers (2018) called ‘ethnographic predictions’, which are a way of making professional judgments about what is likely to happen – and they carry authority because they are grounded in detailed, situated knowledge of people’s lives and practices. Truman (2019) argues that ‘Speculative writing has championed and critiqued advances in science and technology, contemplated gender fluidity and animal rights, marked the ‘more-than-human turn’ across the disciplines, and heralded the ‘posthuman’ in its varied manifestations – cultural, biological, and technological’. Urry (2016) famously differentiated between the possible, the preferable and the probable to make something of and for the future. Extending and troubling the alterations sounds like a good idea, we think, taking into consideration the precarious, at times paradoxical, necessarily posthuman and parallel futures taking shape before our very eyes, line by line. Our authors have partly taken up and partly left untouched the diversity in question.

A Guide to Volume I

Baldeep Kaur writes in their Prediction below, ‘what I know today is insufficient to estimate or grasp the absolute wonders that futures are’. Still, Baldeep adds: ‘We must celebrate the fact that we have futures at all’. Other authors echo this. Across the fifteen texts, Predictions assume multiple forms, ranging from speculative to retrospective, empathetic to those that resist prediction as a mode of control, calling into question the very act of writing such a strange text. At the core of these predictions are a range of object worlds, as we will show in more detail, for example, through the extraction of minerals, planetary depletion, the evolution of AI, data infrastructures and human bodies. We chose to put this centre-stage through single word titles. The driving idea is this: have an entry, have imaginations at hand. The order of the contributions was then selected in such a way that readers are passed on, you move on, sometimes with a deliberately smooth transition, sometimes with a disruption.

All texts are intricately interwoven with histories of power and survival. The writing styles encompass a diversity, too, from manifesto to analyses of failed futures, from speculative ethnography to apocalyptic testimony, from trends to scenarios, reminding us of the different directions of Futures Studies in action, but also invoking a variety of science fiction subgenres breathing life into science and technology. Genres resonating here are cyberpunk’s infrastructural decay, solarpunk’s insistence on alternative futures, a sprinkle of hard sf and a play with honest uncertainty, and dystopian cli-fi’s warnings of environmental collapse. Crucially, the texts also span multiple geographies, making up different worlds, from the extraction landscapes of South America’s Andes to the technological enclaves of Silicon Valley, from the drought-stricken deserts of Arizona to the cybernetic governance of East Asia, touching on European data centres, deep-sea cable networks and the occupied territories of Palestine, each site anchoring prediction within specific planetary realities. Let’s immerse ourselves in these while briefly running through the texts.

Laura Watts’ entry, Energy, offers a bold yet very minute prediction by exploring a tiny piece of the afterlife of energy itself. She presents a speculative vignette, centring on an archaeologist entrusted with overseeing the cremation of digital data – the final traces of a once-celebrated piece of technology. In this future, the conclusion of data is not an erasure but a transformation; information is released as energy into the cosmos, akin to a photon’s journey through deep space. Watts’s work offers a vision where prediction is about reckoning with material and symbolic residues. The conservation of energy becomes a way to think about the persistence of history and the ongoing negotiations between loss and legacy.

Jacqueline Jenkins’ Tourism takes a different direction: it is a prediction framed through retrospective irony, a transcript from the future that looks back on a world where emotions, once uncontrollable, become scarce and commodified. The imagined presentation, delivered in 2124, chronicles how the erosion of affect – driven by environmental collapse, AI companionship and cognitive numbing – paved the way for commercialised emotional revivalism. EmoTerra resorts promise to restore long-lost feelings through curated experiences, a calculated reclamation of human essence. The prediction does not concern the eradication of emotion; rather, it concerns its commercialisation: affect transformed into an elite commodity. What was once spontaneous and embodied is now subject to reconstruction, which may indicate that the most profound predictions are at a planetary scale. The next entry suggests a similar reading.

Tung-Hui Hu’s Poetry offers a future in which language itself becomes a scavenged resource, and if poetry has always been an act of recombination, then in the coming years it will be pushed to the periphery of an AI-dominated linguistic economy. Poets will serve as labourers training machines in emotional nuance. This is just one example of a very temporary intervention. As AI extracts and iterates from past textual forms, what remains for poetry is the discarded – the improbable, the obsolete, the fragments that escape predictive optimisation. Hu’s concept of poetry as a form of resistance against the smooth, automated prose of predictive text is predicated on a turn to the cryptic and the coded.

In Cymene Howe’s Love, the focus lies on the evolution of the relationship between the human and the more-than-human. In this context, love is an expanded mode of kinship, involving an entanglement with both biological and artificial life, with a call to appreciate life forms as such. Predictions made encompass a wide spectrum, ranging from the poetic to the absurd, including the emergence of ecosexuality, the translation of animal languages, the development of AI-generated life forms and the institutionalisation of Harawayism as a spiritual doctrine. The text highlights the coexistence (a multiplicity) of extinctions and new beginnings, emphasising nature’s role as an active participant in shaping the human condition. Howe’s speculative gestures resist linearity, suggesting that the future is a complex network of shifting affinities.

Naomi Okabe’s Caretakers is set in a future (you might have guessed that) in which the remnants of humanity are preserved not by humans themselves, but by artificial caretakers. Similar to Watt, sf runs through the text. The narrative is set in a sanctuary where the last humans are monitored and archived, and the story follows a Lifeboat tasked with recording the memories of Kioku, a thinker who was once a revolutionary. The narrative has an elegiac and pragmatic undertone. Human extinction has already happened, we live through gradual decay, we look back at a record of a bygone era. Still, the act of recollecting these memories is to build a future audience that may, one day, reflect on this moment of planetary reckoning. The caretakers, featuring as historians, find themselves torn between preservation and finality.

Ranjodh Singh Dhaliwal’s Small paints a world governed by predictive systems, where an individual’s every action is measured against a predetermined schedule. The protagonist grapples with a declining predictive alignment score. We get a sense of what it means to be captured by algorithmic oversight, living in a reality where forecasts not only show a future, but influence it in real time. Amid the structured progression of projected efficiencies, a subtle insurrection materialises – modest interventions that challenge the dominance of the predictive system. Dhaliwal’s tale thinks through the potential of minor, unpredictable actions within a society that demands all-encompassing information.

In Blair Attard-Frost’s Realness, the concept of reality itself is imploding. The world is depicted as one where truth, fact and intelligence collapse into their inversions. The prediction brings us closer to a post-reality one. Epistemic authority disintegrates into a play of hallucinations and contradictory superpositions. AI in a different sense. In a world driven by artificial derealisation, glitch feminists, trans activists and dissident epistemic minorities mobilise surrealism as a weapon against the oppressive consensus of majority realities, whatever a ‘majority’ and ‘reality’ actually is. The text functions as both a manifesto and a warning. Prediction is a contested terrain – here we find the power to define realness. We meet and crisscross governance, control and resistance.

In her work Super/Semiconductors, Anne Pasek examines the rise and fall of technological hype through the case of LK-99, the ill-fated room-temperature superconductor that captivated a lazy kind of tech journalism before being debunked. Pasek turns to the political and economic consequences of technological prediction. The text argues that the pursuit of a new materials revolution obscures our capacity, your capacity, to appreciate the realities (sic!) of computation and energy consumption. Instead of anticipating the potential of superconductors, Pasek proposes that we should learn to operate within the constraints of existing technological landscapes. She turns the sceptical luddite into a form of futurism that is pessimistic enough to inform more responsible engagement with technological prediction.

Sebastián Lehuedé’s Minerals is a speculative experiment. It introduces a rebel hiding within the ruins of the Andes, which have been transformed into an automaton-operated mega-mine. The narrative is unfolded through the voice of a ‘Mineralist,’ a faction resisting the historical pact between humans and machines that led to the planet’s slow depletion. This feels very close to home, again. The narrative foresees a future in which humanity’s destiny is not solely determined by artificial intelligence. We live through an entanglement of extraction, automation and resource management. Lehuedé’s vision of the future features the struggles of the past that are embedded within the present infrastructures. Think: sites of future resistance.

Steven Gonzalez Monserrate’s Silence has a melancholic undertone, one of a world shaped by the environmental and infrastructural failures of data capitalism. Again, it is a strong character that enters the stage. We follow an activist who once fought against the unchecked expansion of data centres, only to witness her city succumb to the very forces she opposed. In this text, prediction is the retrospective realisation of what had already been set in motion. The silence of abandoned data centres thus becomes an emblem of loss of the political energy that once sought to resist its collapse. In the end, the protagonist turns her fight into electoral politics. The question that remains is whether this represents a new beginning, or just another cycle of failed engagement?

In Cables, Nicole Starosielski explores the future of submarine cable networks in the context of climate change and digital expansion. Starosielski predicts two key outcomes: first, that the continued growth of the internet will rely on physical infrastructure; and second, that this infrastructure will become increasingly vulnerable to environmental instability. Reminiscent of hard sf, this is a hard prediction. Rising ocean temperatures, shifting sediment flows and deep-sea geopolitical tensions reshape the way global data moves, turning undersea cables into contested sites of economic and political power. This feels likely indeed. The author warns against the optimism of technological continuity, demonstrating how even the most resilient infrastructures are subject to the shifting landscapes of planetary change.

Sun-ha Hong’s Clock examines the grand ambition of the Clock of the Long Now, a project meant to record time for ten thousand years. We are on a different time-scale, as it were. The text introduces the ideological function of long-term thinking in tech culture. Hong critiques the ways in which futurism becomes a means of laundering present-day hubris, with Silicon Valley elites using speculative grandeur to justify their economic and social power. This is the now, and more to come. The contribution traces a lineage of utopian and dystopian forecasts, from AI doomsday scenarios to longtermist rationalisations of elite survivalism. Ultimately, the Clock represents a confluence of monument and ruin, a technological prophecy that, akin to all others, is fated to outlast the predictions that initially gave rise to it.

Taken together, the assembled predictions reflect material cultures of prediction, embedded in the infrastructures, bodies and ecologies that shape our present and constrain our futures. We understand these works as a self-study of futures situated in troubled places.

The publication process of this first volume, along with the second one emerging alongside, encountered the usual hurdles that academic publishing is known for. In this instance, some authors felt an unease with slowness, a growing feeling that parts of some predictions have been challenged; challenging the very craft they have been grappling with. Does this confirm the criticism of predictions themselves, or does it underscore the different rhythms at which the various texts were composed?

One final note on the order of things. Each volume in the Predictions series charts distinct thematic territories signalled by its different subtitles, which imbue each book with a unique atmosphere. This tripartite structure reflects an organising logic that invites readers to explore how different modes of anticipation and speculation shape our understanding of what is possible, desirable, or inevitable. An important part of the Prediction project is its temporal nature. We deliberately went for multiple short editions, getting published in a row. This lets us reflect on developments, look back, and move on.