Methodology

How the edition was made: methods, the model card, the evidence cut and its age, the acceptance record and the glossary.

9 min read0 figures12 tables6 notesNR-XTECH-AI-GLOBAL-2026-BASELINE

Contents

A.1Methods

The global baseline uses published values and four simple derivations. Each is shown in full so that any reader can reproduce it.

Table A.1Methods
No.	Derivation	Calculation	Result
(1)	Growth in data-centre electricity to 2030	945 ÷ 415	≈ 2.3×
(2)	Increase in organisational AI use, 2023–2024	78 − 55	23 points
(3)	Net job change expected by 2030	170 − 92	≈ 78 million
(4)	Compute growth over five years at 4–5× a year	4⁵ to 5⁵	≈ 1,000–3,000×

Table A.2Coupling strength scale (matrix and model card)
Strength	Definition
STRONG	a disruption in the source system has historically changed the target system’s service or output within one season, in more than one region
MODERATE	an effect is documented but buffered or slower
WEAK	an effect is plausible but not documented at global scale

Table A.3Status grammar
Label	Rule
Reported	value as issued by its source, with year
Inferred	reasoned from cited evidence; never a measurement
Estimated	Nexus analytical value with stated method
Scenario	conditional on stated assumptions
Expert input	structured editorial judgement awaiting evidence

A.2Model card, Technology System Model v0

Model ID: MDL-XT01-AI · v0.1
Purpose: represent AI as infrastructure coupled to energy, water, minerals, labour and institutions
Family: graph-based system of systems
Inputs: the cited sources of this edition; no proprietary data
Method: expert-specified coupling strengths (STRONG · MODERATE · WEAK) from assessments and analogue events
Validation: none yet, first validation against federation evidence in 2027
Uncertainty: qualitative; strengths are hypotheses, not measurements
Domain of validity: global scale; not for site, company or model-level use
Reproducibility: strength table and sources published in this edition

20couplings

typed in v0

11strong

documented within one season

5moderate

documented, buffered

4weak

plausible, undocumented

A.3The baseline evidence cut

Every Nexus edition is published from a frozen evidence cut. The global baseline cut is documentary: it pins the exact edition of every source cited. Federation cuts begin with the first national Foundry cuts in 2027.

Cut ID: DC-XTECH-AI-GLOBAL-2026-B0
Type: baseline documentary cut
Resolution policy: pinned · as of 30 September 2026
Sources pinned: 40 cited sources, each at the edition and retrieval date in the references
Model runs: none at baseline, analytical values are stated with their method
Digest: computed at freeze and recorded in the publication record
Supersedes: nothing, first cut of the series
Superseded by: the 2027 AI annual cut

Lineage

01
source edition
02
pinned citation
03
finding
04
page
05
publication record

Table A.4From the baseline cut to the federation cut
Element	Baseline 2026	2027 global annual
Sources	cited global publications, pinned by edition	national, regional and sector cuts through one graph
Analysis	stated methods and editorial estimates	typed analysis specifications
Models	Technology System Model v0 specification	model runs with receipts
Findings	twelve baseline findings	findings re-bound to federation evidence
Review	five passes scheduled	five passes on the exact digest

A.4Evidence age and coverage

A 2026 global edition rests on evidence observed in many different years. This page shows each time base, so that no single year label hides older evidence.

Table A.5Evidence age and coverage
Indicator	Observed	Released	Cadence	Next release
Data-centre electricity use	2024	Apr 2025	periodic	next IEA update
US data-centre share	2023	Dec 2024	periodic	next LBNL report
Organisations using AI	2024	Apr 2025	annual	AI Index 2026
Training compute trend	2010–2024	2024	continuous	continuous
Leading-edge chip capacity share	2019	2021	one-off	new studies
Reported AI incidents	2024	Apr 2025	annual	AI Index 2026
Jobs exposed to AI	2024–2025	2024–2025	periodic	new estimates
People offline	2024	Nov 2024	annual	annual

Table A.6Coverage by system at baseline
System	Coverage	Thinnest dimension
Energy	GOOD	inference against training
Water	THIN	use outside the United States
Compute and cost	GOOD	efficiency of deployed systems
Chips and minerals	PARTIAL	current capacity shares
Adoption	PARTIAL	small firms and public sector
Labour	PARTIAL	measured, not modelled, effects
Safety	THIN	agentic incidents
Governance	GOOD	implementation evidence

A.5Evidence cards, two anchor sources

Issued by the International Energy Agency from data-centre, grid and industry data with scenario modelling to 2030. Role: binds ENE-001 and the 2030 projection, labelled as a projection.

EVD-XT01-001

Energy and AI¹

Issued by Stanford HAI from surveys, public datasets and incident databases. Annual. Role: binds adoption, cost, investment and incident findings.

EVD-XT01-002

Artificial Intelligence Index Report 2025²

Table A.7Quality dimensions at baseline
Dimension	EVD-GLB-001	EVD-GLB-002
Completeness	company disclosure incomplete	survey samples skew to large firms
Timeliness	about a year after the data	about four months after the year
Revision	projections revised with new data	series revised annually
Method transparency	methods and scenarios published	methods published by chapter
External validation	compared with national laboratory estimates	widely cited; no formal audit

Evidence cards to follow in 2027

LBNL data-centre report¹⁵, US electricity and water
Epoch AI compute trends¹⁴, method for frontier compute
International AI Safety Report³, risk taxonomy
ILO exposure index³², occupational exposure method

A.6Publication acceptance record

Editorial self-check at evidence close. Every applicable item carries a disposition; the remaining items are review, approval and deposit.

Table A.8Publication acceptance record
Group	Area	Items	Pass	N/A	Pending
D1	Identity and scope	8	8	0	0
D2	Source and data	12	9	1	2
D3	Analysis and models	12	3	8	1
D4	Findings and claims	9	8	0	1
D5	Figures, tables, maps	10	6	2	2
D6	Editorial and citations	10	8	0	2
D7	Review and approval	13	0	0	13
D8	Artefact and design	12	7	0	5
D9	Registry and release	9	0	0	9

Publication gate

Conditions met: 14 of 23 at evidence close
Blocking problems: none recorded
Open conditions: reviews, approval of the exact digest, deposit, registry pointer

Not applicable at baseline

Model runs: first runs in Q1 2027
Maps: plates in Q2 2027
Waivers: none

A.7Status words

Table A.9The status every value carries
Status	What it means
Observed	Measured directly and published by the source.
Reported	Published statistics of an official or institutional source.
Declared	Stated by the body concerned; not independently measured.
Estimated	A Nexus estimate from cited evidence, with its method in Annex A.
Modelled	Produced by a stated model from cited inputs.
Inferred	Reasoned from cited evidence; not measured.
Forecast	A projection by the cited source.
Simulated	A simulation result under stated assumptions.
Scenario	A conditional statement under stated assumptions; never a prediction.
Expert input	Expert judgement, recorded with its basis.
Candidate	Proposed for a later edition; not yet evidence.
Evidence-quality levels (EQL 1 to 5) grade the source behind a value; they are not a score of the subject.

A.8How the edition is made, released and corrected

Every edition is built from a sealed evidence cut, reviewed under three keys, signed and deposited before any site shows it as published; corrections go to Nexus Standards as change proposals and feed the next cut.

Step	What happens	Source
01 Evidence arrives from the pillars	Foundry seals observations and indicator series, Universe hands over after-action and scenario findings, and contributions are screened at every gateway, including this site’s.
02 The cut is sealed	On the cut date the evidence is pinned under one identifier, so anyone can rebuild the edition from exactly what it was built on.
03 The edition is assembled	The edition is drafted from its template and content pack. Agents may draft; they never publish and never decide.
04 The keys turn	The Global Risks Alliance turns the capital key: its steward checks that no finding reads as advice, a rating or an underwriting decision. No edition moves on until every key has been turned by a named steward.
05 The gate opens	A named approver releases one exact digest of the edition; any change after this point is a new version, never a silent edit.
06 Signed and deposited	The PDF, the web edition and the data bundle are signed together with a manifest and an RO-Crate description, and deposited on Zenodo with a concept and a version DOI.
07 Published on five sites	Only then do the five Reports sites show the edition as published, each reading it through its own lens.
08 Corrections go to Standards	Method and correction findings go to Nexus Standards as change proposals; citations, corrections and new contributions feed the next evidence cut.

A teaching model of the process, not a forecast and not a record of any run; dates and counts on this page come from the programme record. Sources: Reports governance at Global Risks Alliance (2026); Zenodo, repository of record for Reports deposits (2026).

Lifecycle, one state at a time: Draft, Computed, Evidence bound, Reviewed, Approved for release, Published, Corrected, Superseded. This edition is a draft.

A.9Glossary and abbreviations

Table A.10Series terms
Term	Meaning
Baseline Edition	the inaugural annual edition that fixes the reference state for the series
WEFH+B	water, energy, food, health and biodiversity, the national nexus core
Coupling	a typed dependency through which one system transmits to another
Protection gap	the share of losses not covered by insurance
Capability ladder	LISTED → EXISTS → ADEQUATE → TESTED → DEMONSTRATED
EQL	evidence-quality level, 1–5
Evidence cut	a frozen resolution of exact sources and versions
NNC · NLC · NWG	National Nexus Consortium · National Leadership Council · National Working Groups

Table A.11Abbreviations used in this edition
Abbreviation	Expansion
AI	artificial intelligence
AI RMF	NIST Artificial Intelligence Risk Management Framework
BIS	US Bureau of Industry and Security
CETS	Council of Europe Treaty Series
GPU	graphics processing unit, used as an AI accelerator
HAI	Stanford Institute for Human-Centered Artificial Intelligence
IEA	International Energy Agency
ILO	International Labour Organization
ITU	International Telecommunication Union
LBNL	Lawrence Berkeley National Laboratory
TWh	terawatt-hour
UNESCO	United Nations Educational, Scientific and Cultural Organization

A.10Technical annex

Table A.12Technical annex
Item	Annex	Contents	Status
A.1	Indicator definitions	the global indicator set, with units and sources	IN THIS EDITION
A.2	Coupling strengths	the twenty-cell matrix and its rationale	IN THIS EDITION
A.3	Scenario specification	SCN-XT01-CHG-01 assumptions and signposts	IN THIS EDITION
A.4	Capability placements	rung evidence for fifteen functions	IN THIS EDITION
A.5	Model diagnostics	first runs and validation	2027
A.6	Sensitivity analyses	influence of coupling strengths	2027
A.7	Cluster notes	cards for the major data-centre clusters	2027
A.8	Review statements	pass-by-pass review record	AT RELEASE

A.1 · indicator definition, data-centre electricity use

Construct: annual electricity consumed by data centres, including cooling
Unit: TWh a year
Method: IEA bottom-up estimate from server, storage, network and cooling loads
Source: IEA, Energy and AI¹
Uncertainty: company disclosure incomplete; range published
Known limits: does not separate AI from other workloads exactly

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

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
14J. Sevilla and E. Roldán, “Training compute of frontier AI models grows by 4–5x per year.” Epoch AI, 2024.back
3Y. Bengio (ed.), International AI Safety Report. Department for Science, Innovation and Technology, London, January 2025.back
32International Labour Organization (ed.), Generative AI and Jobs: A Refined Global Index of Occupational Exposure. ILO, Geneva, 2025.back

Cite this annex

Valadbaygi, S. (Ed.). (2026). Annex A: Methodology. 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