AI as Social Technology

Introduction

Our debates about ‘AI’ grow out of 1990s science fiction. Back then, Vinge (1993) wrote essays and novels urging us to face up to the oncoming “Singularity”: a moment of rapid change that would fundamentally transform the human condition. On that day, AI would rapidly evolve from merely human-level intelligence, what some now call ‘artificial general intelligence’ (AGI), into something super-intelligent with its own interests and goals. Humanity would then either be casually eliminated by out-of-control machines, or humans would become as gods, with super-human servitors at our command.

However excellent the resulting science fiction (especially Vinge (1992)), it is a scandal that this dream of the ’90s is still alive and shaping debate. For complicated social and cultural reasons (Becker, 2025), many of the progenitors and funders of modern generative AI bought heavily into this mythology, and built their business strategies and innovation around it (Hao, 2025). As Singularity thinking has leaked out of containment, it has fueled speculation about further vast social, political, and economic transformations. Will AI supercharge authoritarian mind-control (Harari, 2018) or remake democracy (Gudiño et al., 2024)? Will neoliberalism become a feral, self-aware, and all-devouring “machinic” system (Land, 2011)? Again, there are excellent science fiction treatments of these and other possibilities (Banks, 1987; Reynolds, 2000; Stross, 2005; Chiang, 2010; McAuley, 2010; Valente, 2011; Emrys, 2022), but novelists are (typically) more careful about incorporating complexities, and few set themselves up as prophets, or even prognosticators.

Authors of speculative non-fiction about AGI are less inhibited, offering sweeping visions of how information technology will completely transform society, economy, politics, or all three. They treat AGI less as a technology than as Andreessen (2023) says, “our alchemy, our Philosopher’s Stone,” an alkahest that will dissolve the dross and cruft of human institutions, leaving only pure, undiluted progress. Prognosticators regularly describe the social institutions that tremble on the brink of transformation in ways that are only slightly less stylized, claiming for example that AI might transport us into Condorcet’s utopia of a new Age of Reason, this time, happily without tumbrils of condemned prisoners waiting on their appointments with the guillotine (Hall, 2026). The result is a genre that we believe is more liable to confuse smart people and lead them astray than to usefully guide public action.

All of these aspirations and arguments are, as we said, rooted in myths which are (at least) twenty years older than the technology which now seems to incarnate them, large language models (LLMs). It is because LLMs moved in the space of a few years from being a technical improvement in machine translation (Vaswani et al., 2017) to being proclaimed as the royal road to AGI that these debates really matter. LLMs are remarkably good generative statistical models of human language (including human-written computer code). This allows them to process language in ways that resemble human discourse and to be jury-rigged to create texts that loosely approximate human reasoning. This is a new material reality, a new force in the world, but one whose actual implications are obscured by the mythic garb it is swaddled in.

If we are unimpressed by stories about paperclip maximizers remaking the galaxy, omniscient bureaucracies of terror or wonder, markets that suddenly become self-aware, and the like, it is not because we think they are too weird. Rather, they are not nearly weird enough, and miss how much of the weirdness is already here. The possible futures we face are much messier and more varied than stark visions of omnipotent AGI, just as our immediate past was. They will be shaped by the collision between imperfect and highly complex technologies and imperfect and highly complex human social systems (Matias, 2023; Nelson, forthcoming). It is impossible to predict the consequences, but we can map, study, and think about them as they are happening.

From our perspective, the Singularity began two centuries ago with the Industrial Revolution (Shalizi, 2010), and it has been much messier and more variegated than anyone could have known. The modern social sciences are the offspring of the enormous shocks that they entailed in the past (Tilly, 1984; Nelson, forthcoming). They now need to work together with computer science and other related disciplines (science and technology studies; communications) to map what is best grasped as another stage in the Long Industrial Revolution. AI may turn out to be very important, but in quite different ways than our inherited myths suggest.

We build on ongoing collaborative work (Farrell et al., 2025) with Alison Gopnik and James Evans which argues that it is a category error to think of “large models” as self-motivated agents in the making. Instead, they are better understood as “cultural” (Yiu, Kosoy and Gopnik, 2024) and “social” technologies, resembling libraries and languages on the one hand and markets and bureaucracies on the other. Here we focus on how to study these technologies’ consequences for human society, emphasizing the social rather than the cultural aspects. We particularly emphasize how AI is a social technology, a systematic means of reorganizing social relationships among human beings (Therborn, 1978). Earlier social technologies include not just other information technologies, but institutions of governance such as bureaucracies, markets, and even democracy (Farrell, 2025). We will focus on LLMs over other AI systems. This downplays some important aspects of modern AI (e.g., its use in straightforward scientific problems such as protein folding) but helps highlight connections to other social technologies.

Briefly: LLMs create social relations between their users and the authors of the text in their training corpora. With the right access to the model and the corpus, one can trace the connections from system output back to individual source texts and their authors (Grosse et al., 2023). These social relations are mechanically mediated, giving users the illusion that they are interacting with just the machine and not an assemblage of people. But mediated social relationships and their illusions are a common fact of modern life. The social relations created by LLMs in turn cut across, and interact with, other social relations, including those shaped by other social technologies.

Our goal here is to clear a common space where the social sciences and computer science and engineering can discuss the social consequences of AI. We draw heavily on the ideas of Simon (1996), who saw AI, political science, administration, economics, computer science, and cognitive psychology as so many branches of the “sciences of the artificial,” studying how human beings create "artifacts" that model, and act on, their environment. From this perspective, AI models are another means of “complex information processing” (Newell and Simon, 1956). As Simon emphasizes, such systems encompass both information technologies, as studied and built by computer scientists and engineers, and social information systems such as markets, bureaucracy, and, although Simon himself does not stress this, democracy (Lindblom, 1965). All such systems process information by reducing complex realities into more tractable ‘coarse-grainings’ or abstractions that (hopefully) capture important features of the data. Producing coarse-grainings is not all that large-scale social institutions do, but it is quite important. Economic, administrative, and political coordination simply cannot work at scale if complex social relationships are not compressed into visible, tractable representations.

This then opens a different perspective on the collision between new technologies such as AI and existing social systems. As DeDeo (2017) suggests, we urgently need to discover how the new coarse-grainings of AI interact with the existing abstractions through which humans simplify an inherently complex world to make it tractable. Both AI and older social technologies are, among other things, forms of information processing. We should investigate how the former are variously reinforcing, reshaping, or replacing the latter.

In the rest of this paper, we briefly situate AI in the historic context of the Long Industrial Revolution. Next, we explore the relationship between social technologies and coarse-grainings or abstractions, emphasizing their lossiness and consequences for power relations between different social groups. That allows us both to describe the apparent strengths and limits of actually-existing AI and start applying Simon’s ideas to the intersection between AI and bureaucracy. Bureaucracy is a crucial, ancient social technology which played a central role in Simon’s work. Its relationship to AI is urgently topical: claims about AGI were seemingly one influence on the Trump administration’s sweeping cutbacks to the administrative state. We contrast these ideas with our own, to draw out the many important questions and problems that are elided or ignored by AGI-fueled speculation. Rather than expecting AGI to resolve perennial problems of human social organization, we should treat AI as a new social technology which will alleviate some problems, exacerbate others, and create new ones, just as other social technologies have done in the past. That, in turn, suggests the urgency of cooperation between social and computer scientists to figure out its social consequences, and broader social and political coordination too, of the kind that happened in previous stages of the Long Industrial Revolution.

Coarse-Grainings and the Long Industrial Revolution

We begin from a different viewpoint on the relationship between AI and society than much existing commentary. Scholars of science and technology studies (STS) are often more interested in how scientific and technological systems reflect broader social and political power relations (or develop their own) than in providing detailed social science microfoundations for their arguments. In contrast, the ‘rationalism’ that has dominated internal debates over AI is microfoundations all the way up. It starts from the assumption that the relationship between human beings and AI agents can be understood through the micro-level lens of strategic competition among rational Bayesian agents, and has only recently begun to think systematically about how collective phenomena might emerge as these systems scale (Hammond et al., 2025).

Herbert Simon’s intellectual project differed from both. His work provides microfoundations for an account of larger social institutions, which is explicitly grounded in the ‘bounds’ to individual human rationality (Simon, 1957). Simon suggests that large scale social technologies emerge from the need of limited humans to build collective arrangements that allow them to map and manage a complex world. Simon’s consistent theme is the mismatch between the complex environments that human beings inhabit and remake, and the limited information processing capacity they have to understand it. Here, Simon’s view partly converges with Dewey’s understanding of democracy (Farrell and Han, 2025). Neo-classical economics tends both to smooth away the complexities of the environment, and to assume that individual humans have unlimited computational power to model it and find optimal solutions to their problems. These assumptions are both mathematically convenient and highly unrealistic. From S

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