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Societal biosecurity – measures built into everyday society to minimize risks from pests and diseases – is an important aspect of managing epidemics and pandemics. We aimed to identify societal options for reducing the transmission and spread of respiratory viruses. We used SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) as a case study to meet the immediate need to manage the COVID-19 pandemic and eventually transition to more normal societal conditions, and to catalog options for managing similar pandemics in the future. We used a ‘solution scanning’ approach. We read the literature; consulted psychology, public health, medical, and solution scanning experts; crowd-sourced options using social media; and collated comments on a preprint. Here, we present a list of 519 possible measures to reduce SARS-CoV-2 transmission and spread. We provide a long list of options for policymakers and businesses to consider when designing biosecurity plans to combat SARS-CoV-2 and similar pathogens in the future. We also developed an online application to help with this process. We encourage testing of actions, documentation of outcomes, revisions to the current list, and the addition of further options.

This post examines whether competition law can remain effective in prospective AI development scenarios by looking at six variables for AI development: capability of AI systems, speed of development, key inputs, technical architectures, number of actors, and the nature and relationship of these actors. For each of these, we analyse how different scenarios could impact effective enforceability. In some of these scenarios, EU competition law would remain a strong lever of control; in others it could be significantly weakened. We argue that despite challenges to regulators‘ ability to detect and remedy breaches, in many future scenarios the effective enforceability of EU competition law remains strong.

Compute or computing power refers to a software and hardware stack, such as in a data centre or computer, engineered for AI-specific applications. We argue that the antitrust and regulatory literature to date has failed to pay sufficient attention to compute, despite compute being a key input to AI progress and services, the potentially substantial market power of companies in the supply chain, and the advantages of compute as a ‘unit’ of regulation in terms of detection and remedies.