Preceding all of this usually comes speculative capital, generated through advanced marketing campaigns, and, of course, the arcane mechanisms of real-estate development. These mechanisms include zoning. Smartness happens in zones—special economic zones, innovation zones, tax free and regulatory free zones. These zones demand capital and they enjoy “extra-statecraft”, not the absence of the state, but the exception to its laws—and its voters (Easterling, 2012).  

Not all these imagined intelligent settlements are envisioned for oligarchs, but they are often still techno-determined and financialized.  Bjark Ingels Group imagines floating cities to provide haven and refuge for urban denizens afflicted by climate change. Scape studios imagined oyster sea-beds defending New York City after Hurricane Sandy from sea level rise while enjoyably being eaten by citizens. They labelled it  ‘oystertecture’. And there are many others that have emerged over the past decade.

Cities have, of course, long been central to politics, technology, and ideas of human subjectivity and agency. The very term city in English derives from the Latin civitas.  The city in Western tradition was imagined as the seat of the demos and the polis  where humans were separated from nature. This separation is  central to the concept  of the political as  realm of human action, and the city as the site in which human agency and politics are enacted.

Smartness rests on the fundamental assumption that such ideas of human agency and spatial containment are now obsolete. The central idea in artificial intelligence is that humans can no longer exercise agency autonomously without a new form of nature—mainly technology.  We might then assume smart cities will not replicate earlier concepts of the urban.

It is perhaps a marker of this new form of territory that, perhaps counter-intuitively, a smart city is not synonymous with a utopian—or even a specific—’form’ of the city. The smart city has no stable ideal form for the foreseeable future. The smart city is constantly evolving through data, and therefore capable of speciation and mutation. The smart city is now modelled as ‘natural’ extending into the seas, integrating local wetlands, becoming ‘planetary’ to use the term in trespassing the boundaries between species, materials, and technical and biological systems.  There is no endpoint to the smart city, it is infinitely extendable and changeable.

In this sense, the smart city is quite unlike utopian cities as they were imagined in recent modernity, when it was presumed that a specific form—such as Le Corbusier’s ‘Radiant City’ or the concentric circles of Ebenezer Howard’s garden cities or the modernism of Brasília—would enable a specific goal, such as integration of humans into natural processes, or colonial domination, or economic growth, or an increase in collective happiness, or democratic political participation. Even the cybernetic cities had forms—round towers, geodesic domes, megastructures.  Ideal forms that should be replicated. These forms might have expanded over the surface of the earth, but they imagined an earth and gave it figurative and spatial forms and limits.

Rather, a city is ‘smart’ when it achieves the capacity to adjust to any new and unexpected threats and possibilities that may emerge from the city’s ecological, political, social and economic environments (a capacity that is generally referred to in planning documents with the term ‘resilience’). In short, a smart city is a site of perpetual learning, and a city is smart when it achieves the capacity to engage in perpetual learning. In the smart city fantasies of directed and agential political revolution are replaced by responsive evolution.

As the smart city constantly adapts, the people who live in it also have to adjust. The inhabitants of a smart city necessarily become perpetual learners. A new language has emerged. People must become ‘innovative’ and ‘resilient’; capable of evolving and adapting to crisis constantly. Resilience is a term taken from ecology, it is about the new nature of technical systems.  This nature is put there to withstand catastrophes, mainly those of politics and climate. Cities also must be resilient.

Smartness and AI guarantee this resilience by managing sea walls, measuring flows of water, people, and resources, and always optimizing—turning limits in resources, environments, and health into frontiers for computing. New organisms possess capacities to compute; i.e fungal networks, bacteria, and bio-materials. Technology has become the new nature, and the smart or intelligent city is now the engine of evolution, not revolution. Societies and political agency do not figure in this vision for capital. Nor does equity or difference.

However, the smart city’s smartness is not supposed to be imposed upon its urban inhabitants from above. Rather, this smartness is supposed to result from the combination of the inhabitants’ unique individual perspectives and choices. Smartness presumes that these acts of combination cannot be accomplished by humans alone, but require the assistance of computing (and now biological) processes—or, more specifically, algorithms that teach the smart city (and its inhabitants) new ways in which to learn. Very much like “the market” of neoliberal economic theory, smartness optimizes processes by combining multiple perspectives in ways that cannot be achieved by any group of human planners. For some of its advocates, the ability of smartness to automate the combination of an enormous number of individual perspectives makes it possible to imagine that politics—that messy realm of self-interest, which often only seems fully open to a select few—can be replaced by technological processes, ones that can actually achieve what democracy only promises.

Smartness is then predicated on the assumption that humans have incomplete information and need the guidance of the market—now social network—or perhaps the state. In smart neoliberal regimes, such networks are assumed to be self-organizing and “free,” but never planned (and certainly not by the government). Others are inverse mirrors, perhaps guided by bureaucratic policy, but still emerging from population-level data through networks. Each instantiation is unique, but all share the premise that that technology manages what the liberal nation-state could not or never did.

Smartness therefore means participation, but not necessarily representation or power. Denizens of a city can offer their data and opinion, but not necessarily control the networks to which they speak. Utopia can never be reached, only versioned. This dream of smartness as a route to freedom (or sovereignty) through surveillance and perpetual learning is the marker of contemporary political economies (platforms), geopolitics, and individual self-fashioning alike.

But learning also offers smartness a faint hope. Such systems might also produce new relations between life and technology. The original cybernetic precept that we only learn in relationship to others might forge possibilities for new relations and networks between humans, geographies, and more-than-human entities.

At the start of the cybernetic revolution, Norbert Wiener raised a concern—what did it mean to learn? In a famous book ‘I am a Mathematician’ written in 1956 at the start of the Civil Rights Movement in the United States of America, Wiener wrote that already at age eleven he had recognized the limits to the idea that mathematical logic might be able to represent the world, and that the systematic and systemic application of rules might equate with education. Education is of course beyond learning, it’s an institutional and social practice, ‘man’s engagement with his environment’ to quote Wiener (Wiener, 1956: 323-325). 

Wiener only repeated a statement made two years before concerning Brown vs. the Board of Education that ‘separate’ education cannot be ‘equal’.  The case put before the courts was that the very institutional and structural separation of black children from white produced a sense of inferiority, and deprived black students of their potential to become part of the demos; to enact political will and to become full citizens.  This reduction of learning to rules, it can be deduced, forestalled the possibility of the United States realizing its potential as a diverse democracy. Simply learning the same thing in a different (and impoverished) institutional setting would not result in the same future.

These concerns appear distant from our present, but they are not. The myth of technocratic solutions to political and social challenges obscures the social context—i.e. histories, ideologies, aesthetics, and institutions—that shape what is learned, how, for whom, and to what ends. To return consciousness to learning—that is to say history, knowledge, difference, and above all an idea of futurity—is the only thing that might also enable equity and democracy. There is no discussion of artificial general intelligence that includes the question of where and how one learns, or that understands learning beyond the ability to adapt to change to an existing condition. There is no concept of smartness that includes projection; the ability to imagine counterfactual and alternative realities. There is no possibility within our technical imaginary of a machine learning that can envision political possibility.   The discourse of smartness has merged technical and bio-determinism and replaced the language of education with that of adaptation and evolution. The idea of change is now in the language of self-organizing systems.

Cybernetics, in turn, made governance explicit. The cybernetic city of the future should be one that does not automate the social, but reflexively seeks to experiment and consciously reflect upon the political. Ultimately, the challenge we face is how to learn in new ways, beyond the parameters of our own conditioning, to envision new forms of connection to the machines, humans, and animals that we share our world with.