Research / Sources
Research on governing advanced AI and its risks. www.governance.ai
Position paper arguing frontier AI governance must go beyond model-level controls, because inference, scaffolding and asset-based capability gains can undermine pre-deployment evaluation and mitigation.
Argues that scaling inference compute instead of pre-training compute could reshape frontier AI governance, weakening training-compute thresholds and open-weight concerns, with ambiguous effects if used during training.
An expert survey (N=166) and public consultation on how thresholds for advanced AI systems should be set, finding broad agreement on multi-stakeholder setting but division over compute thresholds and their number.
Forecasts how many foundation models will exceed EU AI Act and US AI Diffusion compute thresholds by 2028, finding superlinear growth in captured models and a steadier count under relative thresholds.
Proposes a framework for deciding which AI governance issues need international coordination, finding strong benefits for compute oversight, model evaluations, incident monitoring and risk management, and weaker benefits for data privacy and bias.
Argues that training compute is the most suitable initial metric for identifying general-purpose AI models needing regulatory oversight, while noting it is an imperfect risk proxy and should be combined with other measures.
Argues compute providers should act as securers, record keepers, verifiers and enforcers in AI regulation, and analyzes technical feasibility of targeted, privacy-conscious compute oversight under EO 14110 and the EU AI Act.
Argues that governing AI compute, given its detectability, excludability and concentrated supply chain, can advance AI safety and policy goals, while outlining risks and guardrails.
Argues that rising compute efficiency lets more actors train models to a given performance, so large compute investors keep a lead, and governments must prepare for widely available dangerous capabilities.
Proposes that the US government require compute providers to run Know-Your-Customer schemes to give oversight of frontier AI development, export control loopholes, and capability advancements.
A GovAI response to a US government RFI urging frontier AI regulation, including dangerous-capability risk assessments, licensing, standards, and compute governance through export controls and training-run reporting.
A GovAI-co-signed response to the UK's Future of Compute Review arguing for compute governance measures for high-risk AI, including tiered access, training-run reporting, and KYC for compute providers.
GovAI's policy response to the UK Future of Compute Review argues for structured access to high-compute AI models, tiered compute access with review, and research access for outside scrutiny to prevent misuse.
GovAI's submission to the NAIRR Task Force urges structured API access to large pre-trained models and tiered compute review to guard against misuse of AI resources.