Lenses and readings

The AI baseline is read through each steward’s lens, then once as a whole. Each lens is a projection of reviewed findings, not a separate argument.

9 min read0 figures6 tables33 notesNR-XTECH-AI-GLOBAL-2026-BASELINE

Contents

6.1Three lenses

One edition, read by each steward. The lens of this site comes first.

GCRI · KEY T

Technical Truth

The evidence supports reading AI as physical infrastructure with fast-rising, clustered demand for power and water, and a highly concentrated supply chain. Evidence is strongest for electricity use and adoption, and weakest for water, inference energy and agentic failures.

ENE-001 · MIN-005 · SAF-009 · SYS-012

GRF · KEY M

Public Meaning

For most people AI arrives as a tool at work, a change in a job or a new data centre nearby. The baseline asks that public debate weigh the physical and social footprint of AI, power, water, work and access, alongside its benefits.

LAB-007 · DEV-011 · ENE-002

GRA · KEY F

Capital Meaning

Readiness implications only. Investment is large and concentrated, supply risk sits in a few regions, and liability and insurance for AI are nascent. These show where finance-readiness evidence will matter. Nothing here is advice, a rating or an approval.

ADO-006 · MIN-005

NEXUS

Synthesis

AI is scaling faster than the infrastructure and institutions around it. The 2027 programme should measure the physical couplings, test the chip–grid pathway, and build incident evidence for agentic systems before they become critical.

SYS-012 · X-01 · X-04

6.2GCRI reading: the model card

GCRI’s reading of this edition is its model card: what the national model is, what it computes and where it stops. It is printed in full in Annex A, with the methods it belongs to. Annex A, methodology

6.3Global Risks Forum reading: editorial essays

Four signed essays by the Reports editorial team set out the positions this edition takes. They are interpretation, labelled as such; the evidence they draw on is in the findings register.

Every model runs in a building on a grid in a basin. Treating AI as weightless software hides where its demands land: on particular substations, cooling systems and communities. Reading AI by location is the first step to managing it as infrastructure.

EDITORIAL ESSAY · 01

AI has an address

Leading-edge chips are made in very few places, from minerals refined in very few places, and run in very few clouds. Each concentration is efficient; together they form a chain whose weakest link is a single region.

EDITORIAL ESSAY · 02

Concentration is the risk

Systems that act on their own will fail in ways that chat interfaces do not. Before agents become critical to finance, health or infrastructure, the world needs incident evidence about them, not only principles.

EDITORIAL ESSAY · 03

Agents need evidence

About 2.6 billion people are offline. If AI’s benefits reach only the connected, its risks will still reach everyone. Access, skills and language belong in every account of AI readiness.

EDITORIAL ESSAY · 04

Access is the first safeguard

6.4Global Risks Alliance reading: 2027 AI readiness pack

The registers that open the 2027 global cycle. At baseline each is initialised from this edition.

Table 6.12027 AI readiness pack
Item	Register	State at baseline	First 2027 entry
E1	AI risk state delta	baseline set by this edition	changes against the baseline
E2	WEFH+B state delta	five global system readings set	indicator updates and freshness
E3	Agenda and Portfolio movement	eight agenda items · six proposed objects	admission decisions
E4	Federation delivery register	Canada, Global and Water issued	national readiness chapters
E5	Consortium capability register	national consortia forming	first contributions
E6	Leadership questions and challenges	none yet	first challenge log
E7	Foundry technical state	national environments prepared	first federation cut
E8	Publication candidates	twelve findings carried forward	PUBLISH · HOLD dispositions
E9	Universe readiness	one candidate mission	admission and role spine
E10	Correction register	none	first federation-wide cycle

Publication dispositions

Publish
Hold
Needs evidence
Restricted
Superseded

6.5Nexus Standards reading: standards and regulatory watch

Instruments that shape AI development and use, by status on the retrieval date. The edition assesses no one’s compliance.

IN FORCE
ADOPTED
ADOPTED · OPEN FOR SIGNATURE
PUBLISHED · VOLUNTARY

Table 6.2Standards and regulatory watch
Instrument	Body	Scope	Status	Relevance
Regulation (EU) 2024/1689, AI Act⁴	EU	European Union	IN FORCE · PHASED	risk-based obligations
Framework Convention on AI, CETS 225¹⁰	Council of Europe	open to all states	OPEN FOR SIGNATURE	human rights, rule of law
OECD AI Principles⁸	OECD	adherents	ADOPTED · UPDATED 2024	trustworthy AI
UNESCO Recommendation⁹	UNESCO	global	ADOPTED 2021	ethics
Global Digital Compact¹¹	UN General Assembly	global	ADOPTED 2024	digital cooperation
UN scientific panel and dialogue¹²	UN General Assembly	global	ESTABLISHED 2025	evidence and dialogue
NIST AI RMF and GenAI profile⁵⁶	NIST	voluntary	PUBLISHED · VOLUNTARY	risk management
ISO/IEC 42001⁷	ISO · IEC	international	PUBLISHED	management systems
Hiroshima Code of Conduct³⁰	G7	voluntary	PUBLISHED 2023	advanced AI developers

Changed since 2024

EU AI Act prohibitions and general-purpose model rules began to apply⁴
The UN created an AI scientific panel and global dialogue¹²
The Council of Europe convention opened for signature¹⁰

To watch in 2027

Application of high-risk obligations under the AI Act⁴
First outputs of the UN scientific panel¹²
Ratifications of the Council of Europe convention¹⁰

6.6Risk Academy reading: literature and evidence review, science and assessments

The review behind this edition covers water assessments, peer-reviewed hydrology and the institutional reports that bind its findings. Systematic reviews for each agenda item begin in 2027.

Review protocol

Question: What does the published evidence establish about AI as infrastructure and its couplings?
Sources searched: energy and compute analyses; safety assessments; labour studies; governance instruments; peer-reviewed research and labelled preprints
Window: releases available at evidence close, 30 September 2026
Selection: sources that bind or contextualise a finding in this edition
Next step: systematic reviews for agenda items X-01 to X-05 in 2027

Table 6.3Peer-reviewed science and assessments
Reference	Type	Year	Role	Used for
Bengio (ed.)³	assessment	2025	supports	risks of advanced AI
Sevilla and Roldán¹⁴	research note	2024	supports	compute growth
Strubell et al.²³	proceedings	2019	context	training energy
Patterson et al.²⁴	preprint	2021	context	training emissions
Li et al.²⁵	preprint	2023	context	water footprint
Bommasani et al.²⁶	preprint	2021	framework	foundation models
Cazzaniga et al.¹⁶	staff note	2024	supports	labour exposure

Table 6.4Institutional flagships
Reference	Issuer	Year	Role	Used for
Energy and AI¹	IEA	2025	supports	electricity demand
AI Index 2025²	Stanford HAI	2025	supports	adoption, cost, incidents
Data-centre energy report¹⁵	LBNL	2024	supports	US electricity share
Governing AI for Humanity³¹	UN advisory body	2024	context	global governance

Table 6.5Statistics, labour and access
Reference	Issuer	Year	Role	Used for
Generative AI and jobs³²	ILO	2025	supports	exposure
Future of Jobs²⁰	WEF	2025	supports	job churn
Facts and Figures³⁴	ITU	2024	supports	people offline
Technology and Innovation Report³³	UNCTAD	2025	context	development
Global Cybersecurity Outlook²¹	WEF	2025	context	cyber

Table 6.6Supply chains, incidents and electricity
Reference	Issuer	Year	Role	Used for
Semiconductor supply chain¹⁷	SIA · BCG	2021	supports	chip concentration
Critical Minerals Outlook¹⁸	IEA	2025	supports	mineral refining
Export controls¹⁹²⁹	MOFCOM · BIS	2022–23	supports	trade controls
AI Incident Database²⁷	Responsible AI Collaborative	2026	supports	incidents
AI Incidents Monitor²⁸	OECD	2026	supports	incidents
Global Electricity Review²²	Ember	2025	context	power mix

Where the evidence agrees

Data-centre electricity demand is rising fast¹¹⁵
Compute and adoption are scaling quickly²¹⁴
Chip supply is highly concentrated¹⁷

Where it is unsettled

How large inference demand will become as agents spread¹
Whether AI will mainly augment or substitute work¹⁶³²
How much water AI uses at global scale²⁵

Notes

Numbers are the edition’s reference numbers, so a note reads the same in every chapter. The full list, with every source and its licence, is in Annex C. Annex C, references

4European Parliament and Council of the European Union (eds.), Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal L, 12 July 2024, as amended.back
10Council of Europe (ed.), Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law, CETS No. 225. Council of Europe, Vilnius, 2024.back
8OECD (ed.), Recommendation of the Council on Artificial Intelligence, OECD/LEGAL/0449, as amended in 2024. OECD, Paris, 2024.back
9UNESCO (ed.), Recommendation on the Ethics of Artificial Intelligence. UNESCO, Paris, 2021.back
11United Nations General Assembly (ed.), Pact for the Future, resolution 79/1, annex I: Global Digital Compact. United Nations, New York, 2024.back
12United Nations General Assembly (ed.), Resolution establishing the Independent International Scientific Panel on Artificial Intelligence and the Global Dialogue on Artificial Intelligence Governance. United Nations, New York, August 2025.back
5NIST (ed.), Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1. NIST, Gaithersburg, MD, 2023.back
6NIST (ed.), Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1. NIST, Gaithersburg, MD, 2024.back
7ISO and IEC (eds.), ISO/IEC 42001:2023, Information technology, Artificial intelligence, Management system. ISO, Geneva, 2023.back
30G7 Hiroshima AI Process (ed.), Hiroshima Process International Code of Conduct for Organizations Developing Advanced AI Systems. G7, Hiroshima, 2023.back
3Y. Bengio (ed.), International AI Safety Report. Department for Science, Innovation and Technology, London, January 2025.back
14J. Sevilla and E. Roldán, “Training compute of frontier AI models grows by 4–5x per year.” Epoch AI, 2024.back
23E. Strubell, A. Ganesh and A. McCallum, “Energy and policy considerations for deep learning in NLP,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3645–3650, 2019.back
24D. Patterson, J. Gonzalez, Q. Le et al., “Carbon emissions and large neural network training,” arXiv:2104.10350 (preprint, not peer-reviewed), 2021.back
25P. Li, J. Yang, M. A. Islam and S. Ren, “Making AI less ‘thirsty’: uncovering and addressing the secret water footprint of AI models,” arXiv:2304.03271 (preprint), 2023.back
26R. Bommasani, D. A. Hudson, E. Adeli et al., “On the opportunities and risks of foundation models,” arXiv:2108.07258 (preprint), 2021.back
16M. Cazzaniga, F. Jaumotte, L. Li et al., “Gen-AI: Artificial Intelligence and the Future of Work,” IMF Staff Discussion Note SDN/2024/001. IMF, Washington, DC, 2024.back
1International Energy Agency (ed.), Energy and AI. World Energy Outlook Special Report. IEA, Paris, 2025.back
2Stanford Institute for Human-Centered Artificial Intelligence (ed.), Artificial Intelligence Index Report 2025. Stanford HAI, Stanford, 2025.back
15A. Shehabi, S. J. Smith, A. Hubbard et al., 2024 United States Data Center Energy Usage Report. Lawrence Berkeley National Laboratory, Berkeley, CA, December 2024.back
31UN High-level Advisory Body on Artificial Intelligence (ed.), Governing AI for Humanity: Final Report. United Nations, New York, September 2024.back
32International Labour Organization (ed.), Generative AI and Jobs: A Refined Global Index of Occupational Exposure. ILO, Geneva, 2025.back
20World Economic Forum (ed.), Future of Jobs Report 2025. WEF, Cologny/Geneva, January 2025.back
34ITU (ed.), Measuring Digital Development: Facts and Figures 2024. International Telecommunication Union, Geneva, 2024.back
33UNCTAD (ed.), Technology and Innovation Report 2025: Inclusive Artificial Intelligence for Development. UNCTAD, Geneva, 2025.back
21World Economic Forum (ed.), Global Cybersecurity Outlook 2025. WEF, Cologny/Geneva, January 2025.back
17Semiconductor Industry Association and Boston Consulting Group (eds.), Strengthening the Global Semiconductor Supply Chain in an Uncertain Era. SIA and BCG, Washington, DC, 2021.back
18International Energy Agency (ed.), Global Critical Minerals Outlook 2025. IEA, Paris, 2025.back
19Ministry of Commerce of the People’s Republic of China (ed.), Announcement on implementing export control on gallium- and germanium-related items. MOFCOM, Beijing, July 2023.back
29U.S. Department of Commerce, Bureau of Industry and Security (ed.), Implementation of additional export controls: certain advanced computing and semiconductor manufacturing items. Federal Register, October 2022, as updated.back
27Responsible AI Collaborative (ed.), AI Incident Database. incidentdatabase.ai, retrieved 2026-09-30.back
28OECD (ed.), OECD AI Incidents and Hazards Monitor. OECD.AI, retrieved 2026-09-30.back
22Ember (ed.), Global Electricity Review 2025. Ember, London, 2025.back

Cite this chapter

Valadbaygi, S. (Ed.). (2026). Chapter 6: Lenses and readings. In Exponential Technology Report 2026: Artificial Intelligence and Agentic Systems (Baseline Edition 2026, Report No. NR-XTECH-AI-GLOBAL-2026-BASELINE) [Draft under review]. The Global Centre for Risk and Innovation (GCRI).

Every citation form of the whole report, BibTeX and RIS included, is on the edition home. Cite this report