Skip to content

Reports · GCRI · Established

Artificial intelligence · Data and compute

Artificial Intelligence and Autonomous Systems in Reports

Trustworthy artificial intelligence, robotics-enabled autonomy, computational methods and social effects. This page shows the series it feeds, the evidence behind it and how to contribute.

Series
2
Systems
4
Methods
7
01In brief

What this area brings to the editions.

Trustworthy artificial intelligence, robotics-enabled autonomy, computational methods and social effects.

02In the editions

9 findings of the drafted editions that bear on Artificial Intelligence and Autonomous Systems.

Drawn from the Baseline Edition 2026 drafts through the systems this area reads. Each opens at its anchor in its edition, with its numbered sources.

  1. AREUnited Arab EmiratesGas gave 68% of electricity generation in 2025, down from 97% in 2019, while nuclear reached 21% and solar 10%; total generation rose from 138 to 184 TWh.REPORTED · COUNTRY-ARE-ENE-003
  2. ARGArgentinaLow-carbon sources supplied 42% of electricity in 2025, and renewables including large hydro covered 40.2% of demand.REPORTED · COUNTRY-ARG-ENE-003
  3. AUAfrican UnionIn 2024, 55.1% of people in Sub-Saharan Africa had electricity, up from 46.4% in 2018; about 579 million had none (derived).REPORTED · COUNTRY-AU-ENE-002
  4. AUSAustraliaWind and solar supplied 33% of electricity in 2025; fossil fuels supplied 61% and coal 43%.REPORTED · COUNTRY-AUS-ENE-003
  5. BRABrazilRenewables supplied 86.8% of electricity in 2025. Hydropower fell 4.8% to 401.4 TWh while gas generation rose 22.7%, and wind and solar together supplied 26.4%.REPORTED · COUNTRY-BRA-ENE-005
  6. CHESwitzerlandIn 2025 hydropower supplied 55.5% of domestic electricity generation, nuclear power 27.2% and photovoltaics 11.8%, and final electricity consumption was 57,970 GWh.REPORTED · COUNTRY-CHE-ENE-003
  7. CHNChinaCoal supplied 54.8% of electricity in 2025 and 51.4% of total energy consumption, while wind and solar supplied 21.9% of electricity.REPORTED · COUNTRY-CHN-ENE-006
  8. EUEuropean UnionRenewables supplied 25.2% of gross final energy consumption in 2024, 17.3 points below the 42.5% target for 2030.REPORTED · COUNTRY-EU-ENE-003
  9. GS-02Global EnergyGlobal energy demand grew 1.3% (8 EJ) in 2025, down from 2% in 2024, and electricity demand grew nearly 3%, more than twice as fast.REPORTED · SECTOR-ENERGY-GLOBAL-ENE-001
03Evidence path

From Foundry evidence to the series.

The evidence, step by step, from the systems this area reads to the series it feeds.

How Artificial Intelligence and Autonomous Systems reaches Reports: what it reads, how it reads it, the sealed cut and the series it feeds.

StepWhat happensSource
01 What it readsArtificial Intelligence and Autonomous Systems reads NXS-NXQ (software, models and capability lifecycle) and NXS-LAB (research, experiments and simulations): governed sources with their releases, never a value without its source.
02 How it reads themArtificial intelligence and machine learning and AI evaluation and assurance engineering turn the evidence into findings, with assumptions and limits stated beside each value.
03 The cut is sealedEach quarter the evidence is sealed in one cut, so OBSERVED, REPORTED and ESTIMATED values stay apart and every edition can be rebuilt.
04 The series it feedsThe cut feeds Exponential Technology and Labs Evidence, from the Baseline Edition 2026 into the quarterly series.
05 Who decidesDecisions stay with the authorities that hold them, under their own rules; an edition informs a decision and never makes it.

A teaching model of the evidence path, not a forecast and not a record of any run; the systems, methods and series named come from the registries. Sources: Artificial Intelligence and Autonomous Systems in Reports (2026); Artificial Intelligence and Autonomous Systems home (2026).

04Series

The series it feeds, and what backs each.

SeriesFoundry systems it draws onAcademy disciplinesSeries home
Exponential TechnologyNXS-NXQ the capability catalogue and fitness assessments of machine capabilities
NXS-LAB benchmark comparisons and experiment records for fast-changing technologies
Artificial intelligence and machine learning
Innovation and frontier technology governance
Quantum technologies
Exponential Technology series home
Labs EvidenceNXS-LAB what was tried, under which conditions, and what happened, including negative resultsResearch, evidence and the science-policy interface
Complex systems and systems modelling
Labs Evidence series home
08Contribute

Contribute on Artificial Intelligence and Autonomous Systems.

Evidence, a case, a correction, a method or profile input on this subject. The composer tags it with this area and its series.

Contribution composer

Write a contribution the editor can act on.

Say what it is, where its record is kept and on which licence. It arrives tagged with this page, its area and its gateway, so it reaches the editor who owns it.

Your email opens with the subject and a template already filled in for GW-GCRI.

  • Evidence
  • Correction
  • Case study
  • Analysis or method
  • Peer review
  • Translation
  • Profile input

Never send personal data or sensitive infrastructure detail. A contribution is reviewed before use; sending it confers no authorship or editorial rights.

09Open science

Open science for Artificial Intelligence and Autonomous Systems.

How a contribution on this subject is deposited, identified, credited, licensed and found, and the metadata for it prefilled from this page.

Artificial Intelligence and Autonomous Systems’s open record

Area
Artificial Intelligence and Autonomous Systems
Feeds
Exponential Technology, Labs Evidence
Record
A UNI for every accepted contribution; cited with its DOI in the edition that uses it
Links
isSupplementTo the edition it informs; isNewVersionOf its earlier version
Licence
CC BY 4.0 (SPDX CC-BY-4.0) for original material; metadata CC0 1.0

Credit and identity

ORCID
Your iD in the deposit and the contribution record; DataCite can then add the work to your ORCID record
ROR
Your organisation by its ROR identifier, never by a typed name alone
CRediT
Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualisation, Writing, review and editing
Account
None needed to contribute; never a password

Find and follow

Search the open indexes for Artificial Intelligence and Autonomous Systems; software is cited by its SWHID from Software Heritage. Annotation is your choice: Hypothesis opens this page in its own viewer.

Mirrors and harvest

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.

InvenioRDM metadata for a contribution on Artificial Intelligence and Autonomous Systems, prefilled from this page
{
  "metadata": {
    "resource_type": {
      "id": "dataset"
    },
    "title": "Evidence on Artificial Intelligence and Autonomous 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 Artificial Intelligence and Autonomous Systems in Nexus Reports.",
    "rights": [
      {
        "id": "cc-by-4.0"
      }
    ],
    "subjects": [
      {
        "subject": "Nexus Reports"
      },
      {
        "subject": "Artificial Intelligence and Autonomous Systems"
      },
      {
        "subject": "Artificial intelligence"
      },
      {
        "subject": "Data and compute"
      }
    ],
    "related_identifiers": [
      {
        "identifier": "https://therisk.global/nexus-reports/areas/ai-autonomous-systems/",
        "scheme": "url",
        "relation_type": {
          "id": "issupplementto"
        },
        "resource_type": {
          "id": "other"
        }
      }
    ],
    "version": "1.0.0"
  },
  "custom_fields": {},
  "access": {
    "record": "public",
    "files": "public"
  },
  "files": {
    "enabled": true
  },
  "parent": {
    "communities": {
      "default": "nexusreports"
    }
  }
}
10Latest

Releases for this area.

Releases tagged to this area appear here once released.

ReportsContribute

Send evidence on Artificial Intelligence and Autonomous Systems.

Reports is the Consortium’s quarterly publication, written by six desks. This page sits with the Global Centre for Risk and Innovation (GCRI) Systems Evidence desk. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work. Sent to the wrong desk, it is forwarded, not refused.

GCRI Systems Evidence deskArtificial Intelligence and Autonomous SystemsExponential TechnologyLabs Evidence
Reports · GCRI

Bring what you know to the next edition.

Every quarter closes on a sealed evidence cut. Contributions received before the cut are reviewed for that edition; later ones wait for the next.

CC BY 4.0 Free to read. Text and figures: CC BY 4.0. Data: CC0 1.0. Authors keep copyright. Published by GCRI Canada.