Research preview. Independent expert review is pending. The worked analyses are hypothetical.
Method and corrections Interactive field model
Inspect the system around the AI system. The model is organized by the clauses of the definition: societies, the four objects, the benefits and harmful patterns governance aims at, and the conditions it races against. Apply a cross-cutting test, narrow by lifecycle stage or level, and inspect how definitions, claims, relationships and sources travel together.
Apply a cross-cutting test Whole model Power Legitimacy Distribution Adaptability
Narrow by lifecycle stage or level Lifecycle stage Any stage Research Data collection Model development Evaluation Procurement Deployment Monitoring Incident response Retirement Level Any level Community and organisational use Corporate Sectoral National Regional International Loading interactive controls. Concept links remain available. Field model · whole model
Groups follow the clauses of the definition; they are not a sequence or causal chain. Actual relationships appear in the inspector.
Jump to selected entity Societies …how societies should shape…
actor AI developer or provider actor Deploying organization actor Affected people actor Civil society and independent researchers Incentives …the incentives…
incentive Deployment pressure incentive Competitive race dynamics incentive Liability exposure incentive Procurement leverage
Institutions …institutions…
institution Regulator or public authority institution Internal safety and governance team institution Standards body institution International institution
Infrastructures …infrastructures…
infrastructure Evaluation capacity infrastructure Compute infrastructure Data, language resources, and data labor infrastructure Energy, water, and minerals infrastructure Data work and content moderation Safeguards …and safeguards around AI…
safeguard Risk management process safeguard Impact assessment safeguard Traceability safeguard Audit record safeguard Meaningful human oversight safeguard Supervision and regulatory review safeguard Pre-deployment evaluation and red teaming safeguard Incident reporting and post-deployment monitoring safeguard Complaint and appeal routes safeguard Review and sunset provisions safeguard Compute controls Benefits and harmful patterns …so that its benefits are broadly shared and harmful patterns are addressed…
principle Accountability principle Safety principle Broadly shared benefit pattern Contestable decision pattern Concentration of power pattern Systematically unreliable provenance pattern Over-reliance and deskilling pattern Sycophancy and engagement-driven agreement pattern Homogenisation and loss of authorship Choices and conditions …while they are still choices rather than conditions.
condition Lock-in condition Governance lock-in condition Reversibility window
Infrastructure · Infrastructures established
Compute Chips, data centres, and cloud capacity: the concentrated, quantifiable input that determines who can train and serve frontier systems and that governments increasingly use as a governance lever.
Who controls the compute, what does control make visible, and whom does it exclude?
Tests Power Distribution
Lifecycle Model development Deployment
Levels National International Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
Compute — can concentrate → Concentration of power
Mapping: contested
A concentrated, expensive input restricts who can build frontier systems; the UN advisory body describes AI development as concentrating power and wealth globally, and the compute paper flags centralization as a risk.
Inspect evidence · 2 passages Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Governing AI for Humanity: Final Report (opens in a new tab) Executive summary, paras. vii and xv (pp. 7–8)
The accelerating development of AI is said to concentrate power and wealth on a global scale, with geopolitical and geoeconomic implications. Equity is said to demand that more voices play meaningful roles in governance decisions, and the concentration of decision-making in the AI technology sector is described as unjustifiable.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-concentration
Computing Power and the Governance of Artificial Intelligence (opens in a new tab) Abstract
Relative to data and algorithms, AI-relevant compute is presented as a particularly effective point of intervention because it is detectable, excludable and quantifiable and is produced via an extremely concentrated supply chain. Policymakers could use it for regulatory visibility, resource allocation and enforcement; the authors warn that naive or poorly scoped compute governance carries significant risks to privacy, economic impacts and centralization of power, and suggest guardrails.
Scope note: A preprint by authors affiliated with AI developers, universities, and governance organizations; not peer reviewed. Several authors have institutional stakes in compute governance.
Passage checked 2026-09-06 · sastry-compute-abstract
Compute — is the lever for → Compute controls
Mapping: asserted
Because compute is detectable, excludable and quantifiable, it can be the object of reporting thresholds, licensing and export controls in a way data and algorithms cannot.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Computing Power and the Governance of Artificial Intelligence (opens in a new tab) Abstract
Relative to data and algorithms, AI-relevant compute is presented as a particularly effective point of intervention because it is detectable, excludable and quantifiable and is produced via an extremely concentrated supply chain. Policymakers could use it for regulatory visibility, resource allocation and enforcement; the authors warn that naive or poorly scoped compute governance carries significant risks to privacy, economic impacts and centralization of power, and suggest guardrails.
Scope note: A preprint by authors affiliated with AI developers, universities, and governance organizations; not peer reviewed. Several authors have institutional stakes in compute governance.
Passage checked 2026-09-06 · sastry-compute-abstract
Energy, water, and minerals — constrains the siting and growth of → Compute
Mapping: asserted
Data centre electricity demand is projected to more than double by 2030 and is clustered in a few regions, so grid and water capacity shape where compute can be built.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Energy and AI (opens in a new tab) Executive summary — data centre electricity consumption and regional shares
Data centres accounted for around 1.5% of world electricity consumption in 2024, about 415 TWh, growing around 12% a year since 2017. Consumption is projected to more than double to around 945 TWh by 2030. The United States accounted for 45% of 2024 consumption, China 25% and Europe 15%; nearly half of US capacity sits in five regional clusters.
Scope note: Projections are scenario-based estimates, not observations; the 2024 consumption figure is an estimate published in 2025.
Passage checked 2026-09-06 · iea-datacentre-electricity
Claims and evidence · 8 source-assertion Confidence: high Contestability: medium
Compute is an unusually effective governance intervention point because it is detectable, excludable and quantifiable and comes from a concentrated supply chain; compute governance can also threaten privacy, economic outcomes, and centralize power.
Our reading: A preprint position, not a settled finding. Its authors include people at organizations that would operate or be subject to compute governance.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Computing Power and the Governance of Artificial Intelligence (opens in a new tab) Abstract
Relative to data and algorithms, AI-relevant compute is presented as a particularly effective point of intervention because it is detectable, excludable and quantifiable and is produced via an extremely concentrated supply chain. Policymakers could use it for regulatory visibility, resource allocation and enforcement; the authors warn that naive or poorly scoped compute governance carries significant risks to privacy, economic impacts and centralization of power, and suggest guardrails.
Scope note: A preprint by authors affiliated with AI developers, universities, and governance organizations; not peer reviewed. Several authors have institutional stakes in compute governance.
Passage checked 2026-09-06 · sastry-compute-abstract
source-assertion Confidence: high Contestability: low
Data centres consumed roughly 1.5% of global electricity in 2024, and their consumption is projected to more than double by 2030, concentrated in the United States, China and Europe.
Our reading: The 2024 figure is an estimate and the 2030 figure a base-case projection. Data centres serve more than AI; the report attributes the growth surge to AI.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Energy and AI (opens in a new tab) Executive summary — data centre electricity consumption and regional shares
Data centres accounted for around 1.5% of world electricity consumption in 2024, about 415 TWh, growing around 12% a year since 2017. Consumption is projected to more than double to around 945 TWh by 2030. The United States accounted for 45% of 2024 consumption, China 25% and Europe 15%; nearly half of US capacity sits in five regional clusters.
Scope note: Projections are scenario-based estimates, not observations; the 2024 consumption figure is an estimate published in 2025.
Passage checked 2026-09-06 · iea-datacentre-electricity
source-assertion Confidence: high Contestability: medium
Access to compute, data and talent determines who can participate in AI; the UN advisory body proposes a floor under the AI divide rather than universal access to frontier compute.
Our reading: The 'floor' is a proposal, not an existing arrangement.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Governing AI for Humanity: Final Report (opens in a new tab) Executive summary, section C 'Common benefits', paras. xxxviii–xl (p. 14)
Effective participation depends on access to talent, compute and data. Compute is described as one of the biggest barriers to entry; the report says it is unrealistic to promise access to compute that even the wealthiest countries and companies struggle to acquire, and instead seeks to put a floor under the AI divide.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-compute-floor
source-assertion Confidence: high Contestability: low
India answers compute scarcity with public money: its national AI mission funds a public-private compute pool of 10,000 or more GPUs.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Cabinet approves over Rs 10,300 crore for IndiaAI Mission (opens in a new tab) IndiaAI Compute Capacity, first of seven pillars
The Cabinet approved a national AI mission with an outlay of Rs 10,371.92 crore, whose first pillar builds public-private AI compute of 10,000 or more GPUs.
Scope note: Official release of the Union Cabinet decision. Current GPU counts are reported elsewhere and change over time.
Passage checked 2026-10-09 · indiaai-compute-pillar
source-assertion Confidence: high Contestability: medium
Because only a few firms run their own cloud and compute at scale, much of the wider AI ecosystem depends structurally on them.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
2023 Landscape: Confronting Tech Power (opens in a new tab) Executive summary, “Computing Power Advantage” (about p. 7 of the PDF)
Building large AI systems depends on cloud and compute that only a handful of firms run themselves, so other AI companies rely on those incumbents.
Scope note: Civil-society voice. An advocacy report with an explicit position on concentration in the technology sector.
Passage checked 2026-10-09 · ai-now-compute-advantage
source-assertion Confidence: high Contestability: low
The African Union treats Africa’s lack of compute, and reliance on AI and data developed elsewhere, as a structural barrier and a risk to sovereignty.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Continental Artificial Intelligence Strategy (opens in a new tab) Section 1.4.4, p. 28; Executive summary, p. 4
The strategy names the lack of high-performance compute as a main barrier for Africa, notes that all the world’s supercomputers sit in about 30 countries, and that training data usually come from developed countries.
Scope note: Global South regional body. Adopted by the AU Executive Council at its session of 18–19 July 2024 and posted by the AU on 9 August 2024; a strategy, not binding law.
Passage checked 2026-10-09 · au-compute-barrier
source-assertion Confidence: high Contestability: low
India’s AI governance guidelines treat global concentration of compute, data and models as a problem to answer with subsidised access rather than a new AI-specific law.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
India AI Governance Guidelines: Enabling Safe and Trusted AI Innovation (opens in a new tab) Foreword, p. 2; section 2.1, pp. 14–15
The guidelines note that compute, data and models are concentrated among a few global players, and respond by expanding subsidised access to compute and data rather than with a new AI law.
Scope note: Non-binding guidelines; they state that a separate AI law is not needed at present.
Passage checked 2026-10-09 · india-resource-concentration
source-assertion Confidence: high Contestability: low
Chile’s national AI policy says Latin America has been under-represented in setting international AI principles and lacks sovereign high-performance compute.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Política Nacional de Inteligencia Artificial (Actualización 2024) (opens in a new tab) Section 3.2, “Articulación Internacional”, p. 11; Section I, p. 3
Emerging countries, especially in Latin America and the Caribbean, have had little part in setting AI principles, a discussion concentrated in the Global North; the region also lacks sovereign high-performance compute.
Scope note: Adopted by decree; published in the Diario Oficial. Quotes are in Spanish; the passage summary is a translation.
Passage checked 2026-10-09 · chile-global-north-participation