Understanding AI’s societal and technical challenges through transdisciplinary research

Artificial intelligence is often described as a force of nature, an autonomous wave of technological progress that societies must simply adapt to or be swept away by. A new study published in the journal AI & Society rejects that framing outright, arguing instead that the AI revolution is a deeply social, political, and economic phenomenon whose shape and direction are being decided right now by identifiable structures of ownership, labor, and power. The research, authored by Govand Khalid Azeez of Macquarie University’s School of Social Sciences and Vishal Rana of the University of Doha for Science & Technology and Griffith University, offers one of the most sweeping attempts yet to situate the contemporary AI moment within the long history of technology and the political economy of capitalism.

The paper, titled “Decoding the societal and technical challenges of Artificial Intelligence: a comprehensive transdisciplinary approach,” was accepted on 20 May 2026 and published on 3 September 2026. Its central claim is deceptively simple but far-reaching: artificial intelligence is neither the utopian liberation promised by the techno-optimists nor the fatalistic doom feared by the techno-pessimists. Rather, the authors describe AI as a “diachronic dialectical continuum,” meaning that its character, trajectory, and distribution of benefits and harms reflect the social organization, property relations, and democratic arrangements of the societies that produce and govern it. Where those arrangements are unequal, the technology absorbs and amplifies that inequality.

To build this argument, the authors deploy what they call a transdisciplinary materialist framework, synthesizing insights from science and technology studies, political economy, philosophy, and what historians call the longue durée, the long-run history of technology stretching from stone tools through the industrial revolutions to the present. This is not merely an academic exercise in breadth. The framework allows the authors to treat seemingly separate phenomena, such as the mining of critical raw materials, the concentration of semiconductor fabrication, the exploitation of data-labeling labor, and the capture of AI governance by private interests, as dialectically interconnected moments of a single, historically determined techno-societal system. Each element feeds the others; none can be understood in isolation.

The material foundations of the AI conjuncture, as the authors term it, begin with physical infrastructure. The training and deployment of large-scale machine learning models depend on monopolized computational infrastructure, on the extraction of minerals such as those used in advanced chips, and on a semiconductor fabrication and GPU ecosystem concentrated among a handful of state-subsidized corporate actors. The paper points to the extraordinary market dominance of graphics processing units as evidence that the AI economy is not a democratized, distributed commons but a tightly held industrial complex. Projections cited in the article suggest the leading chipmaker could reach a market capitalization measured in the trillions of dollars, a scale of concentration that few industries in history have matched.

Equally central to the analysis is labor. Behind the polished interfaces of generative AI systems lies a global division of work that includes highly paid engineers at one pole and, at the other, precarious data annotation and content-moderation workers in the global South who perform the repetitive tasks that make machine learning possible. The authors frame this as part of a longer pattern of what scholars have called data colonialism, the appropriation of human life and knowledge as raw material for capital accumulation. AI, in this reading, is less an alien intelligence than a privatization of collective human knowledge, a genealogy the paper traces through the social history of computing.

The geopolitical dimension of the study is equally pointed. Drawing on world-systems analysis, which maps the relationship between core and peripheral regions of the global economy, the authors argue that the AI economy reproduces the asymmetric exchange patterns of earlier colonial eras. Computational resources, patents, and profits concentrate in the core, while peripheral geographies supply raw materials, labor, and data, and receive comparatively little of the value generated. China emerges as a notable exception to this pattern, pursuing a state-coordinated AI strategy that includes international cooperation initiatives and algorithmic recommendation regulations, a counterpoint to the market-dominated model of the United States and, more falteringly, Europe with its AI Act.

The paper is also a critique of how AI has been governed, or rather not governed. The authors document what they describe as the structural capture of AI governance by private interests, in which the corporations building the technology largely set the terms of its regulation. They highlight the phenomenon of “ethics washing,” the strategic use of ethical principles and advisory boards to forestall binding rules, and contrast the proliferation of soft-law frameworks, from OECD recommendations to UNESCO’s ethics declaration, with the weakness of enforceable international coordination. Against this backdrop, the paper notes proposals for institutions such as a G20 coordinating committee for AI governance, while stressing that meaningful regulation requires confronting the underlying property relations, not merely the outputs of biased algorithms.

Bias and accountability receive rigorous technical and social treatment. The study reviews the empirical literature demonstrating that machine learning systems absorb and amplify social prejudice: word embeddings encode gender stereotypes, commercial facial-recognition systems show sharply divergent error rates across skin tones and genders, and image generators produce racist and sexist outputs. The authors emphasize that these are not accidental glitches to be patched but predictable consequences of training systems on data drawn from unequal societies and deploying them through concentrated, opaque infrastructures. Algorithmic opacity, the “black box” problem, compounds the difficulty, since the internal reasoning of deep learning systems resists the transparency that accountability demands.

What distinguishes this study from much of the crowded AI ethics literature is its refusal of both dominant emotional registers. The authors explicitly position their argument against the techno-optimist utopianism associated with Silicon Valley manifestos promising abundance and singularity, and equally against the existential fatalism of those who warn that superhuman AI will inevitably destroy humanity. Both framings, they contend, depoliticize the technology by treating its future as predetermined by technical inevitability rather than as the outcome of contestable social choices. Historical perspective supports this view: the benefits of past general-purpose technologies, from electricity to computing, were distributed according to struggles over labor, institutions, and policy, not according to any intrinsic logic of the machines themselves.

The implications of the paper extend to labor markets and development. Citing economic research on automation and employment, the authors note that AI-driven automation both displaces existing tasks and creates new ones, with the balance determined by institutional context rather than technological necessity. Estimates of AI’s macroeconomic impact, including analyses from international financial institutions suggesting that a substantial share of global employment is exposed to generative AI, are read not as prophecy but as a measure of the policy choices ahead. For developing countries, the stakes are particularly high, as the paper’s framework of “dissymmetry” implies that without deliberate intervention the AI economy will widen existing gaps in ownership, access, and capability.

Ultimately, the study is a call to see AI as it is: a material system embedded in capitalism, colonial history, and democratic deficit, but also a system that can be reorganized. The authors argue that because AI’s direction reflects the social body that produces it, changing that direction requires changing the underlying relations of property, governance, and participation. Proposals for digital commons, public computational infrastructure, and genuinely transnational governance are treated not as idealism but as structural necessities. As the AI revolution accelerates through smart cities, epidemiology, gene editing, policing, and even warfare, the paper insists that the decisive question is not what machines will do to us, but what kind of society we will build through them.