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TRUVACE RECORD VERSION
record: TRV-2026-1573
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T10:42:14.875578Z
status: published
lens: p_space
sector: policy
headline: The Global AI Divide: Infrastructure, Access and Policy Pathways for Democratising Artificial Intelligence
dek: Artificial Intelligence (AI) is becoming more accessible via cloud platforms and generative AI tools. However, the skills needed to develop, manage, and regulate advanced AI are still concentrated in a few countries and major tech companies. This study introduces the emerging Global AI Divide, defined as a capability gap driven by unequal access to compute power, advanced semiconductors, data, capital, energy, models, and specialised human expertise. Through qualitative policy analysis and comparative case studi…
gain_title: (none)
problem_title: Skills and resources to develop, manage and regulate advanced AI remain concentrated in a few countries and major tech companies, so wider use of AI tools does not amount to real democratisation.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Skills and resources to develop, manage and regulate advanced AI remain concentrated in a few countries and major tech companies, so wider use of AI tools does not amount to real democratisation.
problem_evidence: the skills needed to develop, manage, and regulate advanced AI are still concentrated in a few countries and major tech companies | wider access to AI applications does not automatically mean true democratisation
quick_read: A qualitative policy analysis with comparative case studies describes an emerging Global AI Divide. It defines this as a capability gap driven by unequal access to compute power, advanced semiconductors, data, capital, energy, models and specialised human expertise, even as cloud platforms and generative tools make AI use easier.

The distinction matters because broad use of AI applications can mask concentrated control over how AI is built and governed. The study offers a framework and names a policy tension between fair access and safe frontier development, but the supplied text reports no measured outcomes from applying that framework.
limitation: The abstract describes a qualitative policy analysis and a proposed framework, and it frames the access-versus-safety tension as an unresolved policy paradox rather than a settled outcome.
tag: Evidence-backed problem
key_points: The study separates democratising AI creation from democratising AI use, arguing the latter alone is not true democratisation. | The divide is defined across compute, advanced semiconductors, data, capital, energy, models and specialised expertise. | It proposes a Six-Pillar Framework covering compute, data, models, innovation, human capability and governance. | It identifies a policy paradox between expanding fair access and keeping frontier systems under safety, accountability and human oversight.
rundown: The research draws on qualitative policy analysis and comparative case studies to examine technological, economic and geopolitical barriers to meaningful AI participation. It treats the issue as a capability gap rather than only a question of who can use AI tools.

The proposed Six-Pillar Framework spans compute, data, models, innovation, human capability and governance. It gives policymakers a structure for thinking about the different layers at which access and control can be unequal.

The authors conclude that democratisation should mean strengthening society's ability to access, adapt, develop and govern AI responsibly, rather than simply increasing consumption of AI products.
sources:
- peer_reviewed | Advances in Social Sciences and Management | https://doi.org/10.63002/assm.405.1779 | 2026-10-14
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