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
Pattern · Benefits and harmful patterns contested
Concentration of power The harmful pattern in which control over compute, data, standards, deployment channels, and political influence accumulates in a few actors in ways that weaken accountability or foreclose alternatives.
Is control over the inputs and the rules becoming concentrated, and does the proposal reduce that or deepen it?
Tests Power Distribution Legitimacy
Levels International National 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 controls — may deepen → Concentration of power
Mapping: contested
Controls that only the largest actors can satisfy reduce access to dangerous capability while consolidating power among those already able to comply.
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
Civil society and independent researchers — contests → Concentration of power
Mapping: hypothesized
Equity is said to demand that more voices play meaningful roles in governance decisions; outside scrutiny is one of the few checks on concentrated decision-making.
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, 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
Claims and evidence · 6 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
International AI governance initiatives leave most countries out: seven countries participate in all of seven sampled initiatives while 118 participate in none.
Our reading: The sample is seven non-UN initiatives chosen by the advisory body and counts endorsement of intergovernmental issuances; membership through the EU or African Union alone was not counted.
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, para. xiv (p. 8); section 3.A 'Representation gaps', paras. 63–65 and figure 8 (pp. 42–43)
Looking at seven prominent non-UN AI governance initiatives, the report finds seven countries party to all of them and 118 countries party to none, primarily in the global South. It calls these representation gaps a problem because the technology is global and comprehensive in its impact.
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-representation
synthesis Confidence: medium Contestability: high
Concentration of power over AI's inputs and decisions is itself a governance problem, not only a side effect of unsafe systems.
Our reading: The UN advisory body treats concentration as a harm; the compute paper treats centralization as a risk of its own proposal. AIGovSpec's framework memo adopts the position that concentration is among the most important harmful patterns; that is an editorial stance.
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
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