AI Scenarios 2030: Helping policymakers plan for the future of AI

Government Office for Science

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AI Scenarios 2030: Helping policymakers plan for the future of AI

June 2026

This is not a statement of policy.

Foreword

Artificial intelligence (AI) has advanced rapidly over the past decade. The most advanced AI systems have shifted from laboratory curiosities to those beginning to reshape our world. They have already transformed fields such as software development and cybersecurity, and millions of ordinary people now use them throughout their day.

However, how this transformation will unfold and what can be done to shape it remains highly uncertain. The Government Office for Science (GO-Science) first developed a set of AI 2030 scenarios in 2023 (published in April 2025) to help policymakers navigate this uncertainty. These scenarios have since been used across government to stress test policies and explore the impacts of AI.

Since 2023, AI capabilities have advanced dramatically, AI investment and adoption have significantly expanded, and a new geopolitical landscape has introduced further uncertainties. To keep pace with these developments, GO-Science, in collaboration with the AI Security Institute (AISI) and the Department for Science, Innovation and Technology (DSIT), has worked with experts from across government, academia, and industry to produce an updated set of scenarios.

These scenarios are tools for exploring uncertainty, stress-testing and developing policy. They are not predictions, and the future may involve elements from all scenarios. It is, however, clear that AI will have a profound impact by 2030.

Our ambition is that these scenarios are used as a shared baseline for cross-government thinking on the future of AI, helping to promote consistency and coherence in long-term planning across His Majesty’s Government. The GO-Science Foresight team stands ready to support departments in applying them.

I would like to thank the many experts who contributed their time and insight to this work.

Professor Dame Angela McLean

Government Chief Scientific Adviser

Image of Professor Dame Angela McLean, Government Chief Scientific Adviser

Executive summary

Introduction

The Government Office for Science (GO-Science) first developed a set of AI 2030 scenarios in 2023, which were published in 2025 (Government Office for Science, 2025). These aimed to help policymakers navigate uncertainty surrounding the future of AI and prepare for its risks and opportunities. They have been used widely across government and are in regular demand.

Since 2023, the AI landscape has changed profoundly, with AI capabilities, investment, and adoption having increased significantly, alongside dramatic shifts in geopolitics. GO-Science has therefore produced an updated set of scenarios to account for these developments, outlined in this report.

Methodology

The scenarios use the same methodology as the previous set from 2023. They are constructed using 6 critical uncertainties – the factors we identified as most influential in shaping the future of AI and those which remain highly uncertain (Table 1). By combining these critical uncertainties in different ways, alongside extensive research and expert judgement, we produced a set of internally coherent, contrasting futures, which we developed into 5 scenario narratives.

The scenarios are designed so that they can be used to test and develop policies; they therefore do not account for any policy intervention from the UK government. They are also not mutually exclusive – the ‘real’ future will include elements from each scenario and could transition from 1 into others.

Table 1: Critical uncertainties

Critical uncertainties

Capability How rapidly will AI improve beyond today’s capabilities? In which domains will it match or exceed humans? Will progress be constrained by inputs like compute, data, or algorithms?

Distribution and model access How concentrated will frontier development be across companies and nations; how durable will advantages be; how accessible or expensive will AI be; how will open systems perform relative to closed systems?

Security Will AI remain controllable, and act as intended; how effectively will malicious actors exploit it?

Adoption How broadly and extensively will AI be used; how much autonomy will we grant it; will the public trust it; what societal impacts will adoption cause?

Labour displacement How will AI impact jobs and labour markets? Will it complement or substitute human labour?

Global cooperation To what extent will nations cooperate on AI; will AI competition and militarisation exacerbate conflict?

Scenarios overview

The table below provides a high-level overview of the 5 scenarios. They are grouped into 3 technological trajectories: whether the rate of advancement in AI capabilities by 2030 will have (1) slowed, (2) continued, or (3) taken off (i.e. rapidly accelerated) based on current trends. Other uncertainties drive divergent futures within these trajectories.

Trajectory 1: AI outperforms humans at a minority of cognitive tasks.

Scenario 1: Slow Burn

AI progress slows, and AI causes less disruption than expected. Limited adoption creates minimal economic uplift, labour displacement is concentrated in certain sectors and roles, and security measures contain most harms effectively.

Six-axis radar chart with uneven performance: strong in Security, moderate in Distribution and Cooperation, but weak in Capability, Adoption and especially Displacement, resulting in a skewed shape.

Scenario 2: Open Frontier

AI progress slows but AI still causes significant disruption. Significant adoption creates modest economic uplift, labour displacement is considerable, and security measures struggle to contain many harms.

Six-axis radar chart with uneven results: heavily weighted towards Distribution, with moderate Adoption and Displacement, but weaker Capability, Security and Cooperation overall.

Trajectory 2: AI matches most humans at most cognitive tasks and outperforms them in certain domains.

Scenario 3: Augmented Growth

AI progress continues, labour markets adapt, and international standards ensure systems are mostly secure. Humans remain ‘in the loop’ for most tasks, many new roles are created, and there is an economic boom.

Six-axis radar chart with broadly balanced results, strongest in Security and Cooperation, solid in Adoption, and comparatively weaker in Displacement.

Scenario 4: Transformation Economy

AI progress continues, and AI causes significant economic disruption. Humans are pushed ‘out of the loop’ for most tasks, causing widespread labour displacement and economic tensions as profits largely accrue overseas.

Six-axis radar chart with mixed results, led by Adoption, Security and Displacement, but notably weaker in Distribution and Cooperation.

Trajectory 3: AI outperforms expert humans at virtually all cognitive tasks, with significant advantages in certain domains.

Scenario 5: Take-Off

AI progress takes off, and misaligned systems pose severe risks. Economic growth is substantial, but labour displacement is widespread and safety is deprioritised amid race dynamics.

Six-axis radar chart with uneven performance, heavily weighted towards Capability and Displacement, with weak results in Security, Cooperation and Distribution.

Key findings

The key findings from our research, expert consultation, and scenario analysis are outlined below.

AI capabilities will continue to increase. As of 2026, AI systems already operate with high autonomy and surpass experts in certain domains. By 2030, they will likely operate even more autonomously and be able to perform a broader range of cognitive and professional tasks. Even in scenarios with a significant slowdown in the rate of AI progress, gains in capability could still be made from finding new ways to integrate and productise AI systems.

AI could deliver widespread positive impacts. This could include significant productivity gains and economic growth, far broader access to highly efficient public services, and accelerated scientific breakthroughs in fields such as health or energy. The opportunity for UK businesses to benefit from AI deployment is substantial, and AI-enabled gains are expected to become the main source of the UK’s continued productivity growth.

AI could cause serious, potentially even existential harms, without government intervention. The impact and scale of existing harms could worsen significantly, and new harms could also emerge. Even without dramatic capability improvements, significant harm could be caused by risks including AI-enabled cyberattacks, AI’s dual-use scientific capabilities, AI systems operating outside human control, or human dependence on AI. As AI systems become more capable, it will also become harder to evaluate their performance and safety.

The potential impact on cognitive labour is significant. AI could cause significant labour displacement by 2030. At the same time, AI is expected to complement and augment some workers, with positive effects on their wages and employment opportunities. Even in futures with lower levels of AI capability or unemployment, the nature of work is likely to change, with routine, execution-oriented tasks increasingly becoming automated.

The frontier AI market is expected to remain highly concentrated toward 2030. A few large technology companies already exert dominance over the development of frontier AI, which is likely to continue or increase further. This scenario would cause a large proportion of the gains from AI accrues to frontier firms, to owners of capital invested in those firms, and to those controlling key inputs, potentially contributing to rising inequality. However, behind the frontier, many AI capabilities are also expected to be increasingly commoditised, with widely available models embedded across a growing range of use cases.

Adoption continues to increase, but the speed, distribution, and extent of adoption are expected to be varied. Commercial and national security imperatives, alongside improvements in the autonomy and reliability of AI systems, drive rapid and extensive adoption in most futures. But at the same time, there are barriers to adoption in all futures, which will result in uneven speeds and levels of adoption. This could exacerbate inequality as certain organisations, sectors, or nations capture disproportionate productivity gains.

Global competition is expected to continue, as economies become increasingly reliant on technology to drive growth and spheres of influence emerge, led by the United States (US) and China. Outcomes for countries outside the frontier will depend on access to technology, partnerships, and the ability to operate within a fragmented global system.

How to use the scenarios

The scenarios are a tool for policymakers to assess and workshop their strategies and policies against. They provide a structured way of exploring plausible AI futures, supporting more robust policy development.

They can be used for policy stress-testing – exploring how different policy responses might perform in different futures, and how they might need to be adapted to achieve their objectives in different contexts. Or, for testing plans and assumptions against un

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