AI reshapes digital geographies as power, extraction and everyday systems converge

The report highlights a growing consensus that AI marks a profound epistemic shift. Rather than acting as neutral computational instruments, AI systems define what is knowable, predictable and governable. Their architectures, shaped by pretraining, probabilistic logic and latent representation, structure how the world is rendered legible. For geographers, this raises foundational concerns: Who sets the parameters of these systems? Whose knowledge is encoded, excluded or amplified? And what forms of visualization, classification and forecasting become dominant in public policy and private platforms?

Artificial intelligence (AI) is reshaping cities, labor, governance and knowledge production at a pace with few historical parallels, and a new discipline-wide report argues that geography is at a pivotal turning point in its engagement with these technologies. The progress report outlines how AI is altering the foundations of digital geographies through extraction, automation and new forms of epistemic power. It calls for geographers to confront both the continuities and ruptures AI brings to the field.

The peer-reviewed article, “Digital geographies II: All about AI. What’s old? What’s new? Where to?” published in Progress in Human Geography, offers one of the most comprehensive assessments to date of how geographers are analyzing AI as an object of study, a methodological tool and a world-making system. It maps the discipline’s longstanding relationship with computational technologies while identifying new stakes that distinguish today’s AI moment from previous digital transformations.

A new technological moment raises old questions about power, knowledge and automation

The 1980s saw early debates about GIS, personal computing and the automation of cartographic work. These discussions asked whether computation would depoliticize geographic knowledge, concentrate power in new institutions and erase the role of human judgment in spatial analysis. The report argues that many of these questions still resonate today.

What has changed, according to the author, is the scale, visibility and social penetration of contemporary AI systems. AI is no longer restricted to specialist research contexts or technical laboratories; it is now embedded in public infrastructure and everyday life. Generative models, robotics, spatial analytics and predictive systems are altering how institutions govern people and places. These systems operate on massive datasets, rely on globalized extractive supply chains and influence decision-making across sectors including policing, urban planning, finance, welfare administration and labor management.

The report highlights a growing consensus that AI marks a profound epistemic shift. Rather than acting as neutral computational instruments, AI systems define what is knowable, predictable and governable. Their architectures, shaped by pretraining, probabilistic logic and latent representation, structure how the world is rendered legible. For geographers, this raises foundational concerns: Who sets the parameters of these systems? Whose knowledge is encoded, excluded or amplified? And what forms of visualization, classification and forecasting become dominant in public policy and private platforms?

The author draws on critical scholarship that sees AI as a continuation of longer regimes of automation, but operating at far greater speed and scale. This new phase intertwines the historical trajectories of digital mapping, database architectures, platform governance and surveillance capitalism. It also introduces new challenges, particularly the capacity of AI to reproduce or intensify inequality through algorithmic bias, racialization and the extraction of labor and resources.

AI as extraction, threat and everyday infrastructure: How geographers are studying the technology

The report identifies three major thematic lenses used by geographers to analyze AI: extraction, existential threat and everyday encounters. Together, these frameworks offer a multifaceted understanding of how AI reshapes environments, institutions and daily life.

The first lens, AI as extraction, highlights the material and political economies that underpin AI development. The author explains that AI systems rely on vast mineral resources, energy-intensive data centers and global supply chains structured by corporate interests. Training datasets draw from billions of online images, texts and user interactions, often harvested without consent. This creates geographies of extraction that extend from lithium mines to server farms, revealing how AI depends on resource flows that reproduce global inequalities. Within this framework, geographers have examined land conflicts around data centers, environmental burdens linked to energy consumption and the invisible labor behind dataset curation and content moderation.

The second thematic lens frames AI as an existential or structural threat, emphasizing how narratives of risk shape public discourse and policy. AI is portrayed as destabilizing jobs, replacing workers, altering the nature of urban life and introducing new forms of vulnerability into governance systems. Yet The author notes that these narratives often obscure deeper systemic forces such as corporate power, austerity regimes and long-standing inequalities. By focusing solely on catastrophic futures, some narratives risk distracting from the social conditions already shaping how AI is deployed.

The third lens focuses on everyday encounters with AI, an area where geographers are increasingly documenting human–machine interactions in public spaces, transportation systems, workplaces and domestic environments. Examples include algorithmic compliance systems in urban governance, automated welfare decision-making, educational technology, smart cameras and service robots. These encounters reveal the frictions, dependencies and uneven geographies produced when AI becomes embedded in mundane routines. The author notes that while AI systems can feel omnipresent, their adoption is patchy, contested and shaped by local conditions, regulatory frameworks and cultural norms.

Across these themes, the report focuses on the rise of multidisciplinary research bridging STS, media studies, postcolonial theory, feminist geography and Indigenous scholarship. These perspectives critique AI’s embedded biases and highlight how data infrastructures reproduce racialized, gendered and colonial dynamics. For geographers, these insights underscore the importance of analyzing AI not only as a technical system but as a product of power relations that shape space and society.

New methods and future directions: How AI is reshaping geographical research

The author examines how AI is transforming geographical research itself. The report identifies a dual methodological shift: AI is simultaneously a tool used by geographers and a subject that requires new research techniques to understand.

On one side, geographers are adopting AI-driven tools for spatial modeling, remote sensing, climate analysis, land-use classification and synthetic data generation. These technologies enable rapid processing of satellite imagery, automated feature detection, predictive mapping and large-scale pattern recognition. AI-augmented spatial analysis increases the speed, resolution and complexity of geographical models, allowing researchers to investigate questions once limited by computational constraints. The rise of GeoAI illustrates how machine learning has become central to identifying spatial patterns in urban environments, ecosystems and hazard zones.

On the other side, geographers are developing new creative, ethnographic and experimental methods to study AI systems themselves. These include robotics ethnographies that observe how automated systems move through public space, collaborative workshops that explore speculative AI futures and artistic methods that make algorithmic processes materially visible. The author argues that these approaches help expose AI’s hidden layers, rendering its infrastructures, failures and biases more legible to the public.

The report highlights the increasing importance of interdisciplinary collaboration. Understanding AI’s spatial impact requires insights from anthropology, political science, computer science, environmental studies and design. The author suggests that such collaborations align with the discipline’s long tradition of cross-field integration and its ability to examine the relationships between technology, power and space.

The progress report poses future-oriented questions about how geographers should navigate the expanding influence of AI. The author argues that geography has a crucial role in shaping the public understanding of AI’s spatial impacts, holding technological systems accountable and redefining ethical approaches to digital innovation. She suggests that some AI systems may require resistance or refusal, especially when they reinforce colonial, extractive or oppressive infrastructures.

The report points to critical posthumanist and Indigenous ontologies as important emerging frameworks. These perspectives decenter human control and imagine more relational and reciprocal approaches to technology. They challenge anthropocentric assumptions that drive AI development and open pathways for alternative digital futures that emphasize environmental care, community autonomy and non-extractive knowledge practices.