Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan

Babar Shahzad Chaudary; Petri Pulli

doi:doi:10.11648/j.stpp.20261002.13

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Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan

Babar Shahzad Chaudary

, Petri Pulli^*

Published in Science, Technology & Public Policy (Volume 10, Issue 2)

Received: 18 September 2026 Accepted: 30 September 2026 Published: 23 October 2026

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Abstract

Artificial intelligence (AI) is increasingly deployed across a continuum ranging from stationary data centers and semi-stationary computing platforms to autonomous vehicles, robots, wearable devices, implants, and Internet of Things nodes. Across this distributed continuum, reliable power supply and the availability of critical raw materials become system-level constraints. This study investigates how power-supply requirements of distributed AI propagate into material and geopolitical dependencies, with particular attention to Pakistan. A Design Science Research approach is applied to a representative wearable-edge-cloud healthcare architecture for assisting visually impaired persons. The artefact is decomposed into power, sensing, communication, computation, and interaction subsystems; representative components are mapped to critical materials; materials are grouped by affinity; and mining, refining, and geographic concentration are examined using peer-reviewed literature and authoritative mineral and energy sources. The analysis identifies recurring dependencies on lithium, graphite, copper, cobalt, rare-earth elements, semiconductor materials and, for prospective hydrogen configurations, platinum-group metals. It also shows that cloud offloading shifts rather than removes material dependency because communication, data center, grid, and backup-energy infrastructures introduce additional upstream requirements. For Pakistan, the study distinguishes documented mineral production and resource development from prospective geological occurrences and downstream refining capability. The results indicate that geological endowment alone does not ensure access to technology-grade materials. The paper therefore contributes a traceable analytical chain linking AI service architecture, power subsystems, component/material dependencies, geographic supply concentration, and geopolitical vulnerability. Scenario analysis is used as a stress test rather than a prediction, highlighting the value of diversified sourcing, processing capability, strategic cooperation, and adaptable system design for resilient distributed AI deployment.

Published in	Science, Technology & Public Policy (Volume 10, Issue 2)
DOI	10.11648/j.stpp.20261002.13
Page(s)	51-67
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Supply Chain, Platinum, Rare-Earth Elements, Energy Metals, Precious Metals, Graphene, Semiconductors

References

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Chaudary, B. S., Pulli, P. (2026). Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan. Science, Technology & Public Policy, 10(2), 51-67. https://doi.org/10.11648/j.stpp.20261002.13

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Chaudary, B. S.; Pulli, P. Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan. Sci. Technol. Public Policy 2026, 10(2), 51-67. doi: 10.11648/j.stpp.20261002.13

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Chaudary BS, Pulli P. Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan. Sci Technol Public Policy. 2026;10(2):51-67. doi: 10.11648/j.stpp.20261002.13

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@article{10.11648/j.stpp.20261002.13,
  author = {Babar Shahzad Chaudary and Petri Pulli},
  title = {Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan},
  journal = {Science, Technology & Public Policy},
  volume = {10},
  number = {2},
  pages = {51-67},
  doi = {10.11648/j.stpp.20261002.13},
  url = {https://doi.org/10.11648/j.stpp.20261002.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.stpp.20261002.13},
  abstract = {Artificial intelligence (AI) is increasingly deployed across a continuum ranging from stationary data centers and semi-stationary computing platforms to autonomous vehicles, robots, wearable devices, implants, and Internet of Things nodes. Across this distributed continuum, reliable power supply and the availability of critical raw materials become system-level constraints. This study investigates how power-supply requirements of distributed AI propagate into material and geopolitical dependencies, with particular attention to Pakistan. A Design Science Research approach is applied to a representative wearable-edge-cloud healthcare architecture for assisting visually impaired persons. The artefact is decomposed into power, sensing, communication, computation, and interaction subsystems; representative components are mapped to critical materials; materials are grouped by affinity; and mining, refining, and geographic concentration are examined using peer-reviewed literature and authoritative mineral and energy sources. The analysis identifies recurring dependencies on lithium, graphite, copper, cobalt, rare-earth elements, semiconductor materials and, for prospective hydrogen configurations, platinum-group metals. It also shows that cloud offloading shifts rather than removes material dependency because communication, data center, grid, and backup-energy infrastructures introduce additional upstream requirements. For Pakistan, the study distinguishes documented mineral production and resource development from prospective geological occurrences and downstream refining capability. The results indicate that geological endowment alone does not ensure access to technology-grade materials. The paper therefore contributes a traceable analytical chain linking AI service architecture, power subsystems, component/material dependencies, geographic supply concentration, and geopolitical vulnerability. Scenario analysis is used as a stress test rather than a prediction, highlighting the value of diversified sourcing, processing capability, strategic cooperation, and adaptable system design for resilient distributed AI deployment.},
 year = {2026}
}

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TY  - JOUR
T1  - Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan
AU  - Babar Shahzad Chaudary
AU  - Petri Pulli
Y1  - 2026/10/23
PY  - 2026
N1  - https://doi.org/10.11648/j.stpp.20261002.13
DO  - 10.11648/j.stpp.20261002.13
T2  - Science, Technology & Public Policy
JF  - Science, Technology & Public Policy
JO  - Science, Technology & Public Policy
SP  - 51
EP  - 67
PB  - Science Publishing Group
SN  - 2640-4621
UR  - https://doi.org/10.11648/j.stpp.20261002.13
AB  - Artificial intelligence (AI) is increasingly deployed across a continuum ranging from stationary data centers and semi-stationary computing platforms to autonomous vehicles, robots, wearable devices, implants, and Internet of Things nodes. Across this distributed continuum, reliable power supply and the availability of critical raw materials become system-level constraints. This study investigates how power-supply requirements of distributed AI propagate into material and geopolitical dependencies, with particular attention to Pakistan. A Design Science Research approach is applied to a representative wearable-edge-cloud healthcare architecture for assisting visually impaired persons. The artefact is decomposed into power, sensing, communication, computation, and interaction subsystems; representative components are mapped to critical materials; materials are grouped by affinity; and mining, refining, and geographic concentration are examined using peer-reviewed literature and authoritative mineral and energy sources. The analysis identifies recurring dependencies on lithium, graphite, copper, cobalt, rare-earth elements, semiconductor materials and, for prospective hydrogen configurations, platinum-group metals. It also shows that cloud offloading shifts rather than removes material dependency because communication, data center, grid, and backup-energy infrastructures introduce additional upstream requirements. For Pakistan, the study distinguishes documented mineral production and resource development from prospective geological occurrences and downstream refining capability. The results indicate that geological endowment alone does not ensure access to technology-grade materials. The paper therefore contributes a traceable analytical chain linking AI service architecture, power subsystems, component/material dependencies, geographic supply concentration, and geopolitical vulnerability. Scenario analysis is used as a stress test rather than a prediction, highlighting the value of diversified sourcing, processing capability, strategic cooperation, and adaptable system design for resilient distributed AI deployment.
VL  - 10
IS  - 2
ER  -

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Babar Shahzad Chaudary

Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

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http://orcid.org/0009-0000-6181-5279
Petri Pulli

Oulu Mining School, University of Oulu, Oulu, Finland

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http://orcid.org/0009-0001-9649-6199

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Chaudary, B. S., Pulli, P. (2026). Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan. Science, Technology & Public Policy, 10(2), 51-67. https://doi.org/10.11648/j.stpp.20261002.13

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Chaudary, B. S.; Pulli, P. Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan. Sci. Technol. Public Policy 2026, 10(2), 51-67. doi: 10.11648/j.stpp.20261002.13

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AMA Style

Chaudary BS, Pulli P. Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan. Sci Technol Public Policy. 2026;10(2):51-67. doi: 10.11648/j.stpp.20261002.13

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@article{10.11648/j.stpp.20261002.13,
  author = {Babar Shahzad Chaudary and Petri Pulli},
  title = {Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan},
  journal = {Science, Technology & Public Policy},
  volume = {10},
  number = {2},
  pages = {51-67},
  doi = {10.11648/j.stpp.20261002.13},
  url = {https://doi.org/10.11648/j.stpp.20261002.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.stpp.20261002.13},
  abstract = {Artificial intelligence (AI) is increasingly deployed across a continuum ranging from stationary data centers and semi-stationary computing platforms to autonomous vehicles, robots, wearable devices, implants, and Internet of Things nodes. Across this distributed continuum, reliable power supply and the availability of critical raw materials become system-level constraints. This study investigates how power-supply requirements of distributed AI propagate into material and geopolitical dependencies, with particular attention to Pakistan. A Design Science Research approach is applied to a representative wearable-edge-cloud healthcare architecture for assisting visually impaired persons. The artefact is decomposed into power, sensing, communication, computation, and interaction subsystems; representative components are mapped to critical materials; materials are grouped by affinity; and mining, refining, and geographic concentration are examined using peer-reviewed literature and authoritative mineral and energy sources. The analysis identifies recurring dependencies on lithium, graphite, copper, cobalt, rare-earth elements, semiconductor materials and, for prospective hydrogen configurations, platinum-group metals. It also shows that cloud offloading shifts rather than removes material dependency because communication, data center, grid, and backup-energy infrastructures introduce additional upstream requirements. For Pakistan, the study distinguishes documented mineral production and resource development from prospective geological occurrences and downstream refining capability. The results indicate that geological endowment alone does not ensure access to technology-grade materials. The paper therefore contributes a traceable analytical chain linking AI service architecture, power subsystems, component/material dependencies, geographic supply concentration, and geopolitical vulnerability. Scenario analysis is used as a stress test rather than a prediction, highlighting the value of diversified sourcing, processing capability, strategic cooperation, and adaptable system design for resilient distributed AI deployment.},
 year = {2026}
}

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TY  - JOUR
T1  - Geopolitical Sustainability of Powering Distributed Artificial Intelligence in Pakistan
AU  - Babar Shahzad Chaudary
AU  - Petri Pulli
Y1  - 2026/10/23
PY  - 2026
N1  - https://doi.org/10.11648/j.stpp.20261002.13
DO  - 10.11648/j.stpp.20261002.13
T2  - Science, Technology & Public Policy
JF  - Science, Technology & Public Policy
JO  - Science, Technology & Public Policy
SP  - 51
EP  - 67
PB  - Science Publishing Group
SN  - 2640-4621
UR  - https://doi.org/10.11648/j.stpp.20261002.13
AB  - Artificial intelligence (AI) is increasingly deployed across a continuum ranging from stationary data centers and semi-stationary computing platforms to autonomous vehicles, robots, wearable devices, implants, and Internet of Things nodes. Across this distributed continuum, reliable power supply and the availability of critical raw materials become system-level constraints. This study investigates how power-supply requirements of distributed AI propagate into material and geopolitical dependencies, with particular attention to Pakistan. A Design Science Research approach is applied to a representative wearable-edge-cloud healthcare architecture for assisting visually impaired persons. The artefact is decomposed into power, sensing, communication, computation, and interaction subsystems; representative components are mapped to critical materials; materials are grouped by affinity; and mining, refining, and geographic concentration are examined using peer-reviewed literature and authoritative mineral and energy sources. The analysis identifies recurring dependencies on lithium, graphite, copper, cobalt, rare-earth elements, semiconductor materials and, for prospective hydrogen configurations, platinum-group metals. It also shows that cloud offloading shifts rather than removes material dependency because communication, data center, grid, and backup-energy infrastructures introduce additional upstream requirements. For Pakistan, the study distinguishes documented mineral production and resource development from prospective geological occurrences and downstream refining capability. The results indicate that geological endowment alone does not ensure access to technology-grade materials. The paper therefore contributes a traceable analytical chain linking AI service architecture, power subsystems, component/material dependencies, geographic supply concentration, and geopolitical vulnerability. Scenario analysis is used as a stress test rather than a prediction, highlighting the value of diversified sourcing, processing capability, strategic cooperation, and adaptable system design for resilient distributed AI deployment.
VL  - 10
IS  - 2
ER  -

Copy | Download