GRF · Innovation Nexus
Frontier technology governance
Frontier technologies such as advanced AI, engineered biology, quantum computing and autonomous robotics develop quickly, cross borders easily and combine in ways no single regulator oversees. Their governance mixes binding law, standards, export controls, voluntary commitments and international dialogue, often under deep uncertainty about capability and risk. This page sets out how governance gaps open at the frontier, how capabilities and controls are tracked, and what the bodies that hold the decisions need.
How the work runs.
Regulatory sandbox board
Frontier technology governance · Regulatory sandbox · Cohort 3
Cohort
| Parametric micro-cover | Insurance | Agreed | 3 of 12 | Testing |
| Flood sensor data market | Data | Partial | 3 of 12 | Conditions |
| AI permit triage | Public service | Agreed | 7 of 12 | Testing |
| Drone deliveries, medical | Logistics | Agreed | 10 of 12 | Exit test |
Exit criteria
- Consumer harm
- none recorded
- Evidence
- independent evaluation
- Rule change
- regulator decides
- Wind-down
- plan on file
Sandbox cycle
- Applyeligibility
- Admitsafeguards
- Testbounded
- Evaluateindependent
- Exitor rule change
- Publishlessons
Fig.Regulatory sandbox board: example names and illustrative values, not a record or a live system.
Frontier technology governance: how the risk develops.
Crossover with AI, biotech, quantum and robotics. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Pace outruns process
Capabilities can change within months while legislation, standards and treaties take years. Rules written for one generation of a technology meet the next, and guidance fills the gap without binding force.
Convergence
AI accelerates biological design, quantum computing threatens current cryptography, and robotics gives software physical reach. Risks emerge at intersections that sit between the mandates of separate regulators.
Dual use and diffusion
The same models, tools and know-how serve beneficial and harmful ends. Openness speeds research and scrutiny and also lowers barriers to misuse, while export controls reach only part of the chain.
Asymmetric knowledge
Developers know more about capabilities and incidents than governments do. Without access, evaluation capacity and reporting duties, authorities govern from public claims and media reports.
People and hazards in China.
Population density, night lights and flood water over China, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
People and hazards, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
People and hazards, framed on China.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NOAA GOES-East ABI GeoColorSources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Live: NOAA GOES-East ABI GeoColor (CIRA). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Training compute of notable AI models | floating-point operations | per model release | Developer disclosures and research trackers | ESTIMATED |
| Jurisdictions with binding AI legislation in force | count of jurisdictions | annual | Authority gazettes and legislative trackers | Published statistics |
| Published international standards on AI, quantum and robotics | count by committee | quarterly | ISO and IEC catalogues | Published statistics |
| Frontier developers publishing safety frameworks | count of developers | annual | Developer publications | DECLARED |
| Export control entries for emerging technologies | count of controlled items | annual | Export control regimes and national lists | Published statistics |
| Reported AI incidents and hazards | count by harm type | monthly | Incident monitors | REPORTED |
| Gene synthesis providers applying sequence screening | count of providers | annual | Industry consortium membership records | DECLARED |
Compare instruments: rules, incentives and information.
Three families of instruments side by side against the baseline, each with its own lag and adoption speed, under the coverage and compliance you set; effect sizes are teaching values to replace with evidence. Scenario: named assumptions about the future, not a forecast. The map on this page shows China; the model itself is not tied to a place.
Controls
- Baseline trend of the outcome
- 2.0% a year
- Coverage of the instrument
- 70%
- Compliance
- 75%
- Effect size, against the teaching value
- 100%
- Years ahead
- 20 years
Readouts
- Largest reduction at the horizon
- Regulation
- Its reduction against the baseline
- 21%
Numbers, methods and sources
| Year | Baseline | Regulation | Economic incentive | Information and capacity |
|---|---|---|---|---|
| 0 | 100.0 | 99.5 | 98.8 | 99.1 |
| 1 | 102.0 | 101.2 | 100.1 | 99.8 |
| 2 | 104.0 | 102.5 | 101.1 | 99.9 |
| 3 | 106.1 | 103.5 | 101.6 | 100.0 |
| 4 | 108.2 | 103.7 | 101.8 | 100.7 |
| 5 | 110.4 | 103.2 | 101.7 | 102.1 |
| 6 | 112.6 | 102.0 | 101.6 | 103.9 |
| 7 | 114.9 | 100.4 | 101.6 | 105.9 |
| 8 | 117.2 | 99.2 | 102.1 | 108.0 |
| 9 | 119.5 | 98.6 | 102.9 | 110.1 |
| 10 | 121.9 | 98.9 | 104.2 | 112.3 |
| 11 | 124.3 | 99.7 | 105.7 | 114.5 |
| 12 | 126.8 | 101.1 | 107.4 | 116.8 |
| 13 | 129.4 | 102.7 | 109.4 | 119.2 |
| 14 | 131.9 | 104.5 | 111.4 | 121.6 |
| 15 | 134.6 | 106.5 | 113.5 | 124.0 |
| 16 | 137.3 | 108.5 | 115.7 | 126.5 |
| 17 | 140.0 | 110.7 | 118.0 | 129.0 |
| 18 | 142.8 | 112.9 | 120.4 | 131.6 |
| 19 | 145.7 | 115.1 | 122.8 | 134.2 |
| 20 | 148.6 | 117.4 | 125.2 | 136.9 |
Methods
- Tools of government Hood (1983), The Tools of Government, Macmillan
- Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
- Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Borders Natural Earth
- Cities Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Innovation Nexus in Foundry
- Tested at Innovation Nexus in Universe
- Codified in Open Infrastructure and Developers in Standards
- Published in Innovation Nexus in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Stanford AI Index Report | Tracking of AI research, models, investment, policy and public opinion | Open, CC BY-ND |
| International AI Safety Report | Independent scientific synthesis on general-purpose AI capabilities and risks | Free to read, terms per publication |
| OECD.AI Policy Observatory and AI Incidents Monitor | National AI policies, compute indicators and incident data | Free, attribution (OECD terms) |
| ISO and IEC catalogues (JTC 1/SC 42 and related committees) | Published and in-progress standards on AI, quantum and robotics | Catalogue free to search; standard texts under paid licence |
| Wassenaar Arrangement control lists | Dual-use goods and technologies subject to export control by participating states | Public document |
| Epoch AI data on notable models | Records of notable AI models, training compute and hardware | Open, CC BY |
| International Gene Synthesis Consortium | Harmonised screening protocol for synthetic DNA orders | Public document |
From mandate to evaluation, by pillar.
The steps an instrument takes from mandate to evaluation, each with the pillar that runs it, and the decisions that stay with the authority. Teaching model with stated illustrative values, not a prediction. The map on this page shows China; the model itself is not tied to a place.
- Mandate and problem framing Competent authority (outside the programme)
A competent authority frames the problem and the mandate under its own rules. Howlett, Ramesh and Perl (2009) - Evidence and system model Foundry
The system is modelled with its feedbacks, data and stated limits. OECD (2014), principle 2 - Options appraised with scenarios Foundry
Instruments are compared under scenarios, with uncertainty shown. Hood (1983) - Exercise with stakeholders Universe
The options are tested in an exercise with those who would implement and bear them. OECD (2014), principle 3 - Standard or guidance drafted Nexus Standards
What works is codified as a standard or guidance, open to comment. Nexus Standards process - Decision by the authority Competent authority (outside the programme)
The authority decides, outside the programme, with the evidence on record. Howlett, Ramesh and Perl (2009) - Capacity and training Risk Academy
Those who implement are trained on the decided instrument and its data. Sendai Framework (2015), priority 2 - Monitoring and public report Reports
Indicators are published with sources, limits and corrections. OECD (2014), principle 5 - Evaluation and revision Nexus Standards
Results return to the standard and the model: the cycle closes. Howlett, Ramesh and Perl (2009)
Methods
- Policy cycle Howlett, Ramesh and Perl (2009), Studying Public Policy, Oxford University Press
- Governance of critical risks OECD (2014), Recommendation of the Council on the Governance of Critical Risks
- Layered drawing of directed graphs Sugiyama, Tagawa and Toda (1981), IEEE SMC 11
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Innovation Nexus in Foundry
- Tested at Innovation Nexus in Universe
- Codified in Open Infrastructure and Developers in Standards
- Published in Innovation Nexus in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
Who decides, and what the evidence serves.
Innovation Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| European Commission AI Office and national AI authorities | How to apply obligations for general-purpose AI models with systemic risk | Capability trackers; comparative safety frameworks; codes of practice |
| National AI safety and security institutes | Which capabilities to evaluate and how to share results with regulators | Comparisons of evaluation methods; capability and incident tracking |
| Export control authorities of Wassenaar participating states | Which emerging technologies to add to control lists, and at what thresholds | Control list comparisons; diffusion and dual-use analyses |
| Standards bodies (ISO/IEC JTC 1, IEEE, CEN-CENELEC) | Where consensus standards can support law and where research is too unsettled to standardise | Standards gap maps against legal requirements |
| Health and biosecurity authorities | How to oversee AI-enabled biological design and DNA synthesis | Convergence scenarios; comparisons of screening practice |
Outputs this topic produces
- Governance maps showing which body covers which frontier risk, and where none does
- Capability and control trackers with every entry labelled by source
- Convergence scenarios for AI with biology, quantum computing and robotics
- Dialogue briefs that let regulators, standards bodies and developers compare frameworks
Exercise a cascade in frontier technology governance with the people who decide.
Assembled from the Nexus Standards area profile Frontier technology risk standards: the hazards it names, the systems and communities that carry the loss, the controls and institutions that dampen it and the data that observe it. Pick an initiating event and step through the cascade to see where mandates meet and which decisions fall to whom. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise for dialogue, not a forecast, an assessment or a position. To model a place of your own choosing, open the full System Builder.
Hazards
- Cryptographically relevant quantum computer
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Data breach (set off downstream)
- Mass account takeover (set off downstream)
- Harvest-now, decrypt-later collection (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H104Hazard family: Quantum technology and cryptographic transition (NHS-1 tech.quantum)NRS-T9Quantum technology and cryptographic transition riskNRS-T0Exponential technology risk and responsible innovationNRS-H109Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)NRS-M19Method fabric: Cyber, AI and frontier technologyNRS-U12Universe platform profile: Foresight Nexus
Observation
- Our World in Data datasets (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Research and development expenditure as a proportion of GDP
- Researchers per million inhabitants
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
What experts ask.
Frontier questions concern speed, convergence and fragmented authority.
- How do advances in one technology accelerate others, for example AI in biological design?
- Which harms emerge from convergence that no single sectoral regulator covers?
- How can governance keep pace without freezing useful development?
- Which international mechanisms exist, and where are the gaps?
- How are dual-use risks balanced against openness in research?
How frontier technology governance is governed: instruments, mandates and commitments.
The instruments, mandates, actors and accountability arrangements a policymaker, diplomat or development agency works with, citing the agreements that set them. Structures follow the frameworks named. Any state or progress drawn is illustrative, not a record.
Policy instrument matrix
The kinds of instrument available on frontier technology governance, by level of government and cooperation.
| Instrument type | Local | National | Regional | Global |
|---|---|---|---|---|
| Regulatory | Zoning, building codes | Laws, standards, licensing | Harmonised rules | Conventions and treaties |
| Economic | Local levies, grants | Budget lines, taxes, insurance schemes | Risk pools, regional funds | Financing mechanisms |
| Information | Local warnings | Disclosure, early warning, statistics | Data sharing arrangements | Global monitoring and reporting |
| Institutional | Local committees | Lead agencies, coordination councils | Regional bodies and secretariats | Multilateral mandates |
| Cooperative | Community agreements | Partnership compacts | Memoranda and joint programmes | Agreements and joint commitments |
Mechanisms and drivers.
Frontier technologies are general purpose: they can be applied across many sectors, which makes them hard to govern through sector-by-sector rules. Convergence multiplies both benefit and risk; AI accelerates materials discovery and biological design, robotics brings AI into physical space, and quantum computing may eventually affect cryptography and simulation.
Governance is spread across regulators, standards bodies, funders, export control authorities and international forums, each with partial mandates. Firms often hold the most technical knowledge. Gaps arise where risks fall between mandates or cross borders faster than authorities can coordinate, and information asymmetry between developers and overseers makes those gaps harder to see.
Why now: AI, biotechnology, quantum and robotics are converging, and governance frameworks for each were written separately. Innovation Nexus convenes the institutions that oversee them to map where the frameworks meet and where none applies, and records the gaps for decision holders.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Horizon scanning and foresight | Emerging developments and convergences | Foresight is not prediction |
| Governance mapping | Which institutions cover which risks | Formal mandates may differ from practice |
| Capability evaluation | What frontier systems can actually do | Evaluations lag rapid development |
| Comparative policy analysis | How jurisdictions approach frontier technology | Approaches change quickly |
How it is studied, and what the methods cannot say.
- 01Artificial intelligence and machine learningCoding policy and legal texts · Mapping patents and papers to technologiesApplied here
- 02Policy, institutional and decision scienceMandate and gap mapping · Programme evaluation design · Comparative regulatory analysisApplied here
- 03Systems engineeringRequirements traceability for AI governance · Readiness gates for transfer programmesApplied here
Questions and answers.
Why use training compute as an indicator?
Who governs frontier technologies internationally?
Does Innovation Nexus evaluate AI models?
Why are frontier technologies hard to regulate?
Is international governance of frontier technology possible?
Does Innovation Nexus evaluate frontier systems?
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