The Global Centre for Risk and Innovation (GCRI)
Static view: the interactive graph runs where this page's scripts and WebGL can; every node is also linked on this page.
Nexus Reports · The Global Centre for Risk and Innovation · Baseline Edition 2026
Nexus Exponential Technology Report · XT-01
Artificial intelligence and agentic systems as physical infrastructure, compute, energy, water, chips, labour, safety and governance, at the 2026 baseline
The record
About this edition
The first annual edition of the AI and agentic systems report. It fixes the 2026 reference state of AI as physical infrastructure, and the model, voice and positions, against which the 2027 edition and every national readiness chapter will be read.
What this edition is
A baseline: the state of AI compute, energy, water, chips, adoption, labour, incidents and governance in 2026, and the couplings through which AI carries risk into power systems, supply chains, work and institutions. It treats AI as infrastructure, not only software.
What it is not
Not a capability ranking of models or companies, a forecast of artificial general intelligence, a safety certification or a policy recommendation. Benchmarks are cited with their dates and methods.
What comes next
National readiness chapters and federation evidence feed the 2027 edition. Each edition reports what changed against this baseline, new measurements, source revisions, method changes, scenario updates and corrections.
Abstract and decision surface
Abstract
Artificial intelligence enters 2027 as physical infrastructure. Data centres used about 415 TWh of electricity in 2024, about 1.5% of the world total, and the IEA projects roughly 945 TWh by 2030, driven by accelerated servers for AI [1]. The training compute of frontier models has grown by four to five times a year [14], while the cost of using a model at a fixed level of performance fell more than 280-fold in two years [2]. Adoption has spread fast: 78% of organisations reported using AI in 2024 [2]. Supply is concentrated: in 2019 about 92% of the world’s most advanced logic-chip capacity was in Taiwan [17], and the refining of key minerals is concentrated in a few countries [18]. Reported AI incidents rose by more than half in 2024 [2], and governance is moving from principles to law, led by the phased application of the EU AI Act [4]. This Baseline Edition fixes the 2026 reference state for AI as a system of compute, energy, water, chips, labour and institutions, and sets the questions the 2027 edition will answer. It ranks no model, company or country.
Headline finding
Data centres used about 415 TWh of electricity in 2024, about 1.5% of the world total, and the IEA projects about 945 TWh by 2030, driven mainly by accelerated servers for AI.
Source: IEA, Energy and AI [1].
Implication
Because demand is clustered, the first effects fall on particular grids, water basins and communities: connection queues, local price pressure and competition for clean power. The global share stays small while local shares become large [1] [15].
At a glance
Six readings that frame the AI baseline, and the eight questions this edition answers, each with its status and source.
How much power does AI use?
Data centres used about 415 TWh in 2024, about 1.5% of the world total, rising to about 945 TWh by 2030 in the IEA base case. [1]
REPORTED
How fast is capability scaling?
Frontier training compute has grown four- to five-fold a year. [14]
REPORTED
Is AI getting cheaper?
The cost of GPT-3.5-level performance fell more than 280-fold in two years. [2]
REPORTED
How concentrated is supply?
About 92% of leading-edge chip capacity sat in one region in 2019. [17]
REPORTED
How widely is AI used?
78% of organisations used AI in 2024. [2]
REPORTED
What does it mean for work?
A quarter to two-fifths of jobs are exposed; exposure is not job loss. [16] [32]
REPORTED
Are harms rising?
Reported incidents reached a record 233 in 2024. [2]
REPORTED
What will 2027 test first?
A chip supply disruption coinciding with grid limits at data-centre clusters. SCN-XT01-CHG-01
EXPERT INPUT
Systems dashboard
ENEEnergy
Data-centre electricity use
415TWh
about 1.5% of world electricity; about 945 TWh projected by 2030.
REPORTED2024IEA [1]
NATNational grid
Data centres’ share of US electricity
4.4%
projected at 6.7–12% by 2028 in the national laboratory’s scenarios.
REPORTED2023LBNL [15]
CMPCompute
Growth in frontier training compute
4–5×a year
sustained since about 2010.
REPORTED2010–2024Epoch AI [14]
ADOAdoption
Organisations using AI
78%
up from 55% in 2023; corporate AI investment reached US$252 billion.
REPORTED2024AI Index [2]
CHPChips
Leading-edge logic capacity in Taiwan
92%
a single-region concentration in the AI supply chain.
REPORTED2019SIA–BCG [17]
SYSIncidents
Reported AI incidents
233in 2024
a record, up 56% on 2023.
REPORTED2024AI Index [2]
Key findings
Twelve findings carry the AI baseline. They are grouped by coupling, never ranked. Each resolves to its source or, where Nexus analysis is involved, to its stated method.
ENEEnergy
CMPCompute
MINMinerals
ADOAdoption
78% of organisations reported using AI in 2024, up from 55% in 2023; corporate AI investment reached about US$252 billion. [2]
LABLabour
Between about a quarter and two-fifths of global employment is exposed to AI, depending on definition. [16] [32]
About one in four jobs worldwide is in an occupation exposed to generative AI, and 3.3% of global employment falls in the highest exposure category; most exposed jobs are more likely to be transformed than eliminated. [20]
GOVGovernance
DEVDevelopment
About 2.6 billion people remained offline in 2024, limiting who can benefit from AI. [34]
SYSSystemic
The leading compound pathway for 2027 testing is an advanced-chip supply disruption coinciding with grid constraints at major data-centre clusters. § scenario
Part I · lead chapter
AI is usually described by what models can do. This baseline describes what AI needs: electricity, water, chips, minerals, networks, skilled people and institutions. Read that way, AI is one of the fastest-growing loads on the world’s physical systems, and one of the most concentrated supply chains in the global economy.
Demand is rising fast. Data centres used about 415 TWh of electricity in 2024, about 1.5% of the world total, and the IEA projects about 945 TWh by 2030 in its base case [1]. In the United States, data centres used about 4.4% of electricity in 2023, with 6.7–12% projected for 2028 [15]. Demand is clustered in a few regions, so its effects on grids and water are local before they are global.
Capability is scaling at a pace few infrastructures have seen. Frontier training compute has grown by four to five times a year [14], while the cost of reaching a fixed level of performance has collapsed: GPT-3.5-level use became more than 280 times cheaper between late 2022 and late 2024 [2]. Cheaper use spreads adoption, 78% of organisations reported using AI in 2024 [2], and spreads the energy demand with it.
Supply is concentrated. In 2019 about 92% of leading-edge logic-chip capacity was in Taiwan [17]. The refining of minerals used in chips and batteries is concentrated in a few countries [18], and export controls on chips, tools and minerals have tightened since 2022 [19] [29]. A single regional disruption could constrain AI capacity worldwide.
Harms are rising with use. Reported AI incidents reached a record 233 in 2024 [2], and the International AI Safety Report documents risks from malicious use, malfunction and systemic effects on labour and information [3]. Agentic systems that act on their own add new failure modes, while incident data on them remain thin.
Governance is moving from principles to law. The EU AI Act applies in phases from 2025 [4]; the Council of Europe opened a binding convention in 2024 [10]; and in 2025 the UN General Assembly created an independent scientific panel and a global dialogue on AI [12]. About 2.6 billion people remain offline [34], so access is itself a governance question.
This edition therefore reads AI as part of the coupled system. The matrix, risk objects, capability ladder and scenario that follow ask the same two questions: where does an AI-driven shock travel next, and who holds the capability to contain it?
Adoption rose by 23 points in a year, faster than most general-purpose technologies. Exposure estimates for jobs vary by definition, so the labour question is how exposure turns into augmentation or substitution [2] [16] [32].
Dependency network
The technology-centred matrix types the twenty couplings between the five core systems, read for the demands and effects of AI. Strengths are Nexus estimates from the cited evidence; they set the graph that measurement will test in 2027.
Select a system
Each line is one coupling, weighted by the strength the edition assigns. Strengths are Nexus estimates from the cited evidence and analogue events.
| WAT | ENE | FOO | HLT | BIO | |
|---|---|---|---|---|---|
| WAT Water | · | STRONGhydro · thermal cooling | STRONGirrigation · rain-fed yield | STRONGsafe water · sanitation | STRONGflows · wetlands |
| ENE Energy | MODERATEpumping · treatment · desalination | · | STRONGfuel · fertiliser · cold chain | STRONGair pollution · cooling | MODERATEland use · mining |
| FOO Food | STRONG≈70% of withdrawals | WEAKbioenergy feedstock | · | STRONGnutrition · food safety | STRONGland conversion |
| HLT Health | WEAKworkforce | WEAKworkforce | MODERATElabour · disease | · | WEAKresidues |
| BIO Biodiversity | STRONGregulation · filtration | MODERATEbiomass · hydrology | STRONGpollination · soils | MODERATEzoonoses · medicines | · |
Transmission paths
Reinforcing loops · both directions rated strong
A shock entering either system of a loop returns amplified through the other. A causal-loop reading of the matrix (Sterman, Business Dynamics, 2000): structural, from estimated strengths, not a measured feedback.
Domain posture
Each of the twelve coupled domains is read through AI, in words, not scored. The coupling column names the dependency through which AI carries risk into each domain.
| Domain | Posture at baseline | Coupling | Status |
|---|---|---|---|
| WAT | Cooling water demand rising at data-centre clusters; global volumes not consolidated. | AIC → WAT | EXPERT INPUTEQL 2 |
| ENE | About 415 TWh in 2024, projected to about 945 TWh by 2030. | AIC → ENE | REPORTEDEQL 4 |
| FOO | AI in agriculture is early; supply-chain optimisation spreading. | AIC → FOO | EXPERT INPUTEQL 2 |
| HLT | Clinical and research uses grow; safety and bias concerns documented. | AIC → HLT | INFERREDEQL 2 |
| BIO | Monitoring uses grow; physical footprint adds land and water pressure. | AIC ↔ BIO | EXPERT INPUTEQL 2 |
| CLI | AI aids forecasting; data-centre emissions depend on the power mix. | AIC ↔ CLI | INFERREDEQL 3 |
| INF | Data centres become critical infrastructure; grid connection queues lengthen. | AIC → INF | REPORTEDEQL 3 |
| FIN | Corporate AI investment about US$252 billion in 2024; valuations concentrated. | AIC → FIN | REPORTEDEQL 3 |
| DAT | Cloud and model concentration create common points of failure. | DAT ↔ AIC | INFERREDEQL 3 |
| AIC | Compute scaling four to five times a year; costs falling fast. | the core system | REPORTEDEQL 3 |
| MIN | Chips and their minerals concentrated in a few countries. | MIN → AIC | REPORTEDEQL 3 |
| TRD | Export controls on chips, tools and minerals since 2022. | TRD → AIC | REPORTEDEQL 4 |
Sector screens
All sixteen sectors are screened for their exposure to AI, on the same grammar as every edition. The evidence column describes coverage of the baseline evidence, not the performance of the sector.
| Code | Sector | Baseline evidence | Exposure to watch | Evidence coverage |
|---|---|---|---|---|
| GS-00 | Cross-system | AI couples energy, water, minerals and labour [36] | concentrated common dependencies | PARTIAL |
| GS-01 | Water | cooling demand at clusters [37] | local water competition | THIN |
| GS-02 | Energy | about 1.5% of electricity [1] | grid strain at clusters | GOOD |
| GS-03 | Food | early adoption | supply-chain dependence on AI tools | THIN |
| GS-04 | Health | clinical and research uses | safety, bias, privacy | PARTIAL |
| GS-05 | Finance | investment about US$252 billion [2] | valuation concentration | PARTIAL |
| GS-06 | Development | 2.6 billion offline [34] | unequal access to benefits | GOOD |
| GS-07 | Climate | emissions depend on power mix [1] | demand growth against clean supply | PARTIAL |
| GS-08 | Disaster | AI in forecasting and response | over-reliance; failures under stress | THIN |
| GS-09 | Infrastructure | data centres as critical assets | connection queues; single points of failure | PARTIAL |
| GS-10 | Cities | clusters in a few metro regions | local grid and water pressure | PARTIAL |
| GS-11 | Biodiversity | monitoring uses | land and water footprint | THIN |
| GS-12 | Earth observation | AI-assisted analysis growing | model and data provenance | PARTIAL |
| GS-13 | Cyber and digital | AI changes attack and defence [21] | automated attacks on infrastructure | PARTIAL |
| GS-14 | Policy | binding AI law has begun [4] [10] | implementation capacity | GOOD |
| GS-15 | Science | AI accelerates research | reproducibility and dual use | PARTIAL |
Risk portfolio
Four AI risk objects open the portfolio. Order carries no rank. Each is read across driver, exposure, vulnerability, capacity, cascade and consequence.
Capability ladder
Existence is not readiness. Each of the fifteen capability functions is placed on the ladder by the strongest public evidence for AI at the global level.
Ladder: LISTED, EXISTS, DEMONSTRATED, TESTED. A capability listed in policy is not a tested one; each rung is the strongest public evidence at baseline.
Scenario · SCN-XT01-CHG-01
A conditional statement under stated assumptions, never a prediction.
Assumptions
Cascade, step by step
How the shock in scenario SCN-XT01-CHG-01 would travel through Artificial Intelligence and Agentic Systems's coupled systems, step by step, under the assumptions the edition states.
Leading-edge chip output falls sharply. Analogue or source: 2021 shortage. Edition XT-01, scenario SCN-XT01-CHG-01, step 1
The cascade of scenario SCN-XT01-CHG-01 as a playable chain of couplings, one step at a time.
A teaching model of a conditional scenario under the edition's stated assumptions: not a forecast, not a prediction and not a warning.
Sources: AI and Agentic Systems 2026, scenario SCN-XT01-CHG-01 (2026); AI and Agentic Systems 2026, references (NR-CIT-1) (2026).
Three lenses
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.
GCRI · KEY T
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
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
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
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
The Global Centre for Risk and Innovation reading
2027 agenda
Eight bounded questions open the 2027 AI agenda. Each comes from a baseline finding, names the initiative that will carry it and the first output due in the 2027 edition.
Where will data-centre demand exceed grid and water capacity first, and what does that do to local prices and reliability?
How exposed is AI capability to a disruption in advanced chips, tools or minerals?
How much energy does inference use as AI agents spread, compared with training?
Which failure modes of agentic systems appear in real incidents, and how are they caught?
Is AI augmenting or substituting work, by occupation and region, on measured rather than modelled evidence?
How is the EU AI Act being applied, and what evidence of compliance capacity exists?
How does AI change the cyber threat landscape for critical infrastructure?
Which AI capabilities in risk management have been tested in real operations?
Portfolio proposed for the Q1 2027 alignment
Five initiatives and one Universe mission candidate are proposed for the 2027 AI portfolio. They are programme objects, no partner, funder or member is attached to them in this edition.
| ID | Object | Kind | Question | State |
|---|---|---|---|---|
| INI-XT01-GRD-01 | Compute–grid–water capacity | Initiative | X-01 | PROPOSED |
| INI-XT01-CHP-02 | Chip and mineral supply concentration | Initiative | X-02 | PROPOSED |
| INI-XT01-INF-03 | Inference energy measurement | Initiative | X-03 | PROPOSED |
| INI-XT01-SAF-04 | Agentic incident evidence | Initiative | X-04 | PROPOSED |
| INI-XT01-LAB-05 | Measured labour effects | Initiative | X-05 | PROPOSED |
| MSN-XT01-2027-01 | Chip–grid compound Universe mission | Mission | X-02 | CANDIDATE |
The pathway
The AI edition reads national readiness chapters, the sector reports on energy, water and cyber, and the global edition through one governed evidence graph. At baseline the federation is designed; Canada, Global and Water are issued.
National readiness chapters
AI and compute in each national edition
Sector couplings
GS-02 energy · GS-01 water · GS-13 cyber
Technology system model
compute, energy, chips, labour, governance
Strategic links
SR-13 strategic technology · SR-04 minerals · SR-10 DPI
Nexus Universe
annual proving environment
XT-01 report
this series
Evidence and correction
one graph, many projections
System chapter
AI capability rests on compute, and compute rests on data centres: buildings full of accelerators that need power, cooling, land and connectivity.
State at baseline
| Component | State | Evidence | Status |
|---|---|---|---|
| Training compute | four to five times a year since about 2010 | [14] | REPORTED |
| Inference | demand rising as use spreads; poorly measured | [1] | INFERRED |
| Cost | cost of fixed-level use collapsing | [2] | REPORTED |
| Concentration | compute concentrated in few providers and regions | [1] | INFERRED |
Couplings
Evidence to bind in 2027
System chapter
AI demand lands on particular grids and basins. The global share of electricity is small; local shares at clusters are large and rising.
State at baseline
| Component | State | Evidence | Status |
|---|---|---|---|
| Electricity | about 1.5% of world use | [1] | REPORTED |
| Local grids | connection queues at clusters | [15] | REPORTED |
| Water | cooling demand at water-stressed sites | [25] [37] | INFERRED |
| Emissions | depend on the power mix of each grid | [1] [22] | REPORTED |
Couplings
Evidence to bind in 2027
System chapter
The AI supply chain is among the most concentrated in the world, from mineral refining to chip fabrication.
State at baseline
| Component | State | Evidence | Status |
|---|---|---|---|
| Fabrication | leading-edge capacity in one region | [17] | REPORTED |
| Minerals | refining concentrated in a few countries | [18] | REPORTED |
| Trade controls | chips, tools and minerals since 2022 | [19] [29] | REPORTED |
| New capacity | fabs under construction elsewhere | [17] | INFERRED |
Couplings
Evidence to bind in 2027
System chapter
AI is spreading through work faster than evidence on its effects. Exposure is broad; whether it brings augmentation or substitution is the open question.
State at baseline
| Component | State | Evidence | Status |
|---|---|---|---|
| Adoption | 78% of organisations in 2024 | [2] | REPORTED |
| Exposure | a quarter to two-fifths of jobs | [16] [32] | REPORTED |
| Job exposure | About one in four jobs exposed to generative AI; 3.3% in the highest category | [20] | REPORTED |
| Measured effects | limited causal evidence so far | , | EXPERT INPUT |
Couplings
Evidence to bind in 2027
System chapter
Harms are rising with use. Agentic systems that act on their own add new failure modes, while incident evidence remains thin.
State at baseline
| Component | State | Evidence | Status |
|---|---|---|---|
| Incidents | record 233 reported in 2024 | [2] [27] | REPORTED |
| Risk taxonomy | malicious use, malfunction, systemic risks | [3] | REPORTED |
| Cyber | AI changes attack and defence | [21] | INFERRED |
| Agentic failures | little systematic evidence | , | EXPERT INPUT |
Couplings
Evidence to bind in 2027
Process
Press play to run the release step by step in the five pillar lanes; select a step to read it.
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.
Nexus Foundry seals observations and indicator series, Nexus Universe hands over after-action and scenario findings, and contributions are screened at every gateway, including this site's.
The quarterly release in the five pillar lanes: what Foundry and Universe hand to Reports, what Reports seals, reviews, signs, deposits and publishes, and what returns to Standards.
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: Nexus Reports governance at GCRI Canada (2026); Zenodo, repository of record for Nexus Reports deposits (2026).
How a contribution reaches this edition, step by step, is on the contribute page and the front page of this site. Contribute
Open science
Identifiers, licences and the bundle at deposit; credit by ORCID, ROR and CRediT; the open indexes; and a deposit prefilled for a contribution to this edition.
Search the open indexes for Artificial Intelligence and Agentic Systems; software is cited by its SWHID from Software Heritage, and a quantified scenario run is preregistered on OSF before it is run. Annotation is your choice: Hypothesis opens this page in its own viewer.
The DOI is canonical. At release each signed bundle is also given an IPFS content identifier and a bloxberg anchor as mirrors; both are listed in the record once they exist. Zenodo exposes every record over OAI-PMH, and reviews and endorsements can arrive through COAR Notify where the repository supports it.
{
"metadata": {
"resource_type": {
"id": "dataset"
},
"title": "Evidence on Artificial Intelligence and Agentic Systems for Nexus Reports",
"publication_date": "YYYY-MM-DD",
"creators": [
{
"person_or_org": {
"type": "personal",
"family_name": "Your family name",
"given_name": "Your given name",
"identifiers": [
{
"scheme": "orcid",
"identifier": "0000-0000-0000-0000"
}
]
},
"affiliations": [
{
"id": "ROR identifier of your organisation"
}
]
}
],
"description": "What it is, where its record is kept and how it bears on AI and Agentic Systems 2026.",
"rights": [
{
"id": "cc-by-4.0"
}
],
"subjects": [
{
"subject": "Nexus Reports"
},
{
"subject": "Artificial Intelligence and Agentic Systems"
},
{
"subject": "Exponential Technology Report · XT-01"
}
],
"related_identifiers": [
{
"identifier": "https://therisk.global/nexus-reports/editions/xt-01/",
"scheme": "url",
"relation_type": {
"id": "issupplementto"
},
"resource_type": {
"id": "publication-report"
}
}
],
"version": "1.0.0"
},
"custom_fields": {},
"access": {
"record": "public",
"files": "public"
},
"files": {
"enabled": true
},
"parent": {
"communities": {
"default": "nexus-reports-gcri"
}
}
}Outputs
What is on this site now, what ships with the signed bundle at release, and what is not recorded for this edition; then the chain the outputs come from and how to cite each one.
| Format | State | What it is | Identifier |
|---|---|---|---|
| Web editionHTML | On this site | Draft in stewardship review; every value resolves to its source. | NR-XTECH-AI-GLOBAL-2026-BASELINE-v1.0.0 |
| Print editionPDF | At release | A4 portrait · screen, print and archival PDF, signed with the web edition at release. | NR-XTECH-AI-GLOBAL-2026-BASELINE-v1.0.0 |
| Indicator dataCSV · JSON | On this site | 15 indicators with unit, year, status and source; the machine-readable copy ships with the evidence bundle at release. | NR-XTECH-AI-GLOBAL-2026-BASELINE data release v1 |
| FiguresSVG · PNG | On this site | 4 on the web edition: headline figure, figure 1.3, coupling instrument, capability ladder. | FIG-XTECH-AI-GLOBAL-2026-BASELINE-* |
| SlidesPDF | Not recorded | No slide deck is recorded for this edition. | |
| Evidence briefHTML · PDF | Not recorded | No evidence brief is recorded for this edition. | |
| Dataset with DOIDataCite | At release | Concept and version DOI are minted at deposit; none is recorded yet. |
Provenance
Cite it
Indicator data
| Indicator | Value | Unit | Year | State | Source |
|---|---|---|---|---|---|
| Data-centre electricity use | ≈415 | TWh | 2024 | REPORTED | [1] |
| Share of world electricity | ≈1.5 | % | 2024 | REPORTED | [1] |
| Projected data-centre use | ≈945 | TWh | 2030 | SCENARIO | [1] |
| US data-centre share of electricity | 4.4 | % | 2023 | REPORTED | [15] |
| Frontier training compute growth | 4–5× | a year | 2010–2024 | REPORTED | [14] |
| Fall in cost of fixed-level use | >280× | fold | 2022–2024 | REPORTED | [2] |
| Organisations using AI | 78 | % | 2024 | REPORTED | [2] |
| Corporate AI investment | ≈252 | US$ billion | 2024 | REPORTED | [2] |
| Reported AI incidents | 233 | incidents | 2024 | REPORTED | [2] |
| Leading-edge logic capacity in Taiwan | 92 | % | 2019 | REPORTED | [17] |
| Jobs exposed to AI, IMF | ≈40 | % | 2024 | REPORTED | [16] |
| Jobs exposed to generative AI, ILO | ≈25 | % | 2025 | REPORTED | [32] |
| Employment exposed to generative AI | 25 | % | 2025 | REPORTED | [20] |
| Employment in the highest exposure category | 3.3 | % | 2025 | REPORTED | [20] |
| People offline | 2.6 | billion | 2024 | REPORTED | [34] |
Six pillars
Nexus Foundry seals the evidence and Nexus Universe tests it; Nexus Reports publishes it; Nexus Standards takes its corrections and Risk Academy teaches from it.
| Handoff | Between | What crosses, and when |
|---|---|---|
| F03 | Nexus Foundry to Nexus Reports | Sealed observations and indicator series behind a profile (closeout). |
| F04 | Nexus Universe to Nexus Reports | After-action and scenario findings for chapters 10 and 13 (after action review). |
| F05 | Nexus Foundry to Nexus Reports | National Portfolio movement for chapter 11 (after action review). |
| Ring | Nexus Standards to Nexus Reports | Nexus Standards: applied in editions. |
| F06 | Nexus Reports to Nexus Standards | Method and correction findings as change proposals (correction). |
| F12 | Nexus Reports to Nexus Universe | Q4 synthesis informs the next-year mandate (renewal). |
| Ring | Nexus Reports to Risk Academy | Risk Academy: taught in the open. |
Corrections log
A correction is a new version with a published notice; the earlier version stays resolvable.
0correction notices. No edition has been released yet, so none can be corrected; the log below lists each edition's current version.
| Edition | Version | Date | State | Change | Notice |
|---|---|---|---|---|---|
| XT-01 | NR-XTECH-AI-GLOBAL-2026-BASELINE-v1.0.0 | October 2026 | Draft | Baseline Edition 2026, inaugural annual edition | None |
Notice types: erratum, addendum, supersession, withdrawal. A correction is a new version with a published notice; the earlier version stays resolvable, and no edition is ever edited in silence.
References · NR-CIT-1
Publication record
Publication
Publisher
Colophon
Release path for this version
Release path for this version
| Step | What happens | When |
|---|---|---|
| Draft | editorial self-check complete at evidence close | October 2026 |
| Steward review | five passes on the exact draft (keys T · M · F · N) | Q4 2026 |
| Approval | publication authority approves the exact digest | before release |
| Deposit | concept and version DOI minted; registry pointer set | at release |
| Succession | the 2027 AI edition reports changes against this baseline | 2027 |