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Economic Prosperity

Accelerating the Future: Industrial Strategy in the Era of AI


Paper26th March 2024

Our Future of Britain initiative sets out a policy agenda for a new era of invention and innovation. This series focuses on how to deliver radical-yet-practical solutions – concrete plans to reimagine the state for the 21st century – with technology as the driving force.


Chapter 1

Executive Summary

The United Kingdom has a growth problem.

Real wages haven’t risen since 2008 and GDP per person is set to fall over the course of this Parliament. Part of the challenge is that the UK doesn’t have a strategy[_] for creating growth, nor for removing the drags on growth that come from an economy based on technology[_] and intangibles.[_]

As the economist Erik Brynjolfsson states, “What can we do to create shared prosperity? The answer is not to slow down technology. Instead of racing against the machine, we need to race with the machine. That is our grand challenge.”[_]

For the UK and other economies, generating shared prosperity requires a new approach to industrial strategy that will accelerate the innovation, adoption and diffusion of AI. Critical to this new model is the recognition that AI is not merely a new technology but an upstream enabler of productivity and competitiveness in the UK’s wealth-generating and job-creating sectors.

An AI-era industrial strategy represents a significant departure from traditional approaches to industrial policy, which have often focused on supporting specific sectors or places. In addition, traditional industrial policy instruments – such as subsidies and trade protections – may have limited effectiveness in an era characterised by rapid change, uncertainty and complex feedback loops between technology, the economy and society.

Realising the full potential of AI requires a new set of policy instruments that embrace a more agile and adaptive approach to policymaking – one that is guided by experimentation, learning and iteration. This piece sets out what a framework for an AI-era industrial strategy should look like, and priorities within this overall framework include:

  • A coordinated and proactive approach, powered by a centre of government where science-and-technology expertise are deeply embedded

  • Investment in the critical infrastructure that can be leveraged by multiple industries and applications, as well as the removal of barriers to the flow of knowledge, data, talent and capital

  • The use of regulatory sandboxes, data trusts and disruptive-innovation labs to accelerate the development and deployment of AI-era technologies

  • New forms of procurement and co-investment models that can share the risks and rewards of AI development and deployment across the economy

  • Developing AI-powered UK businesses across a range of sectors that can grow into giants of the world economy

The UK has been too slow to recognise previous technological waves and too ponderous to understand how to take advantage of them. Given the state of the UK economy and the country’s advantages in AI, it cannot afford to be slow in benefiting from this new technological revolution.


Chapter 2

The New Technological Revolution Is Changing Growth

For the UK, growth and prosperity aren’t just about solving the problems of today, but also hastening the technological advances of tomorrow. Science and technology have been the driving force of progress for much of our modern age. Our accomplishments have allowed us to live longer, healthier lives, to travel across the world (and into space), and to generate food and energy at scale.

The UK has been at the forefront of many of these breakthroughs and was home to one of humanity’s great leaps: the Industrial Revolution. Another revolution is now taking place, with developments in artificial intelligence (AI) set to revolutionise various sectors by enhancing productivity and fostering innovation. This is pivotal for long-term prosperity, especially as the UK seeks to boost productivity and economic growth.

Recent developments in AI have already delivered significant improvements across a wide range of tasks, be they drafting emails, code writing or diagnosing medical conditions; this demonstrates its potential to substantially impact the future of work across the economy. Early studies have highlighted its efficacy in doubling coding speeds for software engineers and enhancing productivity by 14 per cent in call centres, as well as the possibility of automating or augmenting up to 49 per cent of work tasks.

Beyond immediate day-to-day gains, AI is already accelerating scientific discovery in biotech and materials.[_] For example, researchers recently discovered 2.2 million new crystal structures – a finding that advances progress in fields from solar cells to superconductors and is equivalent to almost 800 years of experimentation. If pulled through to practical applications, these discoveries could potentially double economic output over the next 20 years, surpassing current growth projections.[_]

However, challenges such as slow adoption due to organisational inertia, skill deficits, regulatory hurdles and the difficulty of measuring knowledge-work productivity pose barriers to realising generative AI’s full benefits. Despite concerns over AI exacerbating inequality, its widespread integration into business and its user-friendly nature underscore its broad and transformative impact.

The challenge for policymakers in the AI era is to mitigate the negative impacts while fully embracing potential productivity opportunities. However, this requires a fundamental reordering of priorities and the way the state itself functions, including how it designs and delivers an industrial strategy.

At its most basic level, industrial strategy is about how governments can support – and even accelerate – economic transformation. Yet, too often, the UK’s approach to industrial strategy has been rooted in the technological transformations of the past.

New technologies are already transforming the UK’s manufacturing and service sectors. Old industrial centres are using their engineering and science skills to become new digital hubs. Some of the fastest-growing sectors in Manchester are advanced materials, data infrastructure and biotech; in Leeds it’s AI and data systems, fintech and edtech; and in Bristol it’s robotics, gaming and data systems.

The UK must reimagine its approach to industrial strategy and embrace the AI revolution in two ways. First, it must put science and technology at the heart of government decision-making. Second, it must ensure that AI is being used to accelerate innovation and competitiveness in its wealth-generating sectors.


Chapter 3

The AI-Era Tech Revolution Is Accelerating

The global economy is going through three economic transitions: the AI tech revolution, the energy transition and the US-China decoupling.

The AI-driven tech revolution has initiated a new era, with recent developments creating a self-reinforcing loop of accelerated innovation. More data are being produced, accumulated and analysed than ever before. This has led to a new generation of AI, with processes and services being automated, science and research accelerating – and more disruption to come. Improved satellite connectivity will make this revolution available in even the remotest areas, as seen in recent pilots between Starlink and the governments of Rwanda, Malawi and Zambia, brokered by the Tony Blair Institute (TBI). Greater coverage creates more sources of data and sustains progress.

Figure 1

Key elements of the AI-era tech revolution

Key elements of the AI-era tech revolution

The second transformation is the renewable-energy transition. Energy prices have been relatively constant for decades but the ability to generate this resource at lower costs – and use it much more efficiently – would unlock many secondary benefits. These would include dropping the cost of transport and energy-intensive manufacturing dramatically, while also lifting millions out of energy poverty.

This will not depend on one single advance: the technology areas supporting our climate response will need to cut across traditional disciplines, ranging from novel materials and AI algorithms through to chemistry and atomic physics. This energy transition is deeply connected to the success of the AI revolution, as the ability to scale AI depends on the supply of cheap, renewable energy to expand compute capacity. In turn, AI capabilities are critical to discovering the new materials and creating the optimisation systems required for the renewable-energy transition.

The third economic transformation comes as a result of the rising tensions between the United States and China. Amid escalating disagreement over trade and technology, as well as geopolitical disputes, the US is trying to diversify supply chains, enforce technology export controls and boost domestic production in critical sectors. These efforts seek to safeguard national security and ensure economic stability by lessening dependency on Chinese goods and technology.

However, there are concerns over the potential decoupling of the world’s two largest economies, which could affect global markets and innovation. Both nations have responded with policies to strengthen domestic capabilities and minimise foreign reliance, highlighting the delicate balance between reducing risks and maintaining a functional bilateral relationship. This strategic shift reflects broader efforts to navigate the complexities of global interdependence and competitive dynamics, particularly when it comes to critical minerals, the skills and capabilities that are related to the AI revolution, and energy transition in the form of semiconductors and electric batteries.


Chapter 4

Weve Been Here Before

Fifty years ago, the information and communication technologies (ICT) revolution began to radically alter global economic landscapes by both revolutionising business operations and employment, and transforming the ways in which people worked and communicated. These advances enabled the use of robots for the automation of routine tasks as well as allowing jobs to be moved offshore, which kickstarted a new wave of globalisation.

Communities with the economic infrastructure to benefit from knowledge-based sectors thrived. However, communities that were dependent on industrial sectors were left stunted and ravaged, with many still struggling today.[_]

This dynamic has ultimately led to a highly unequal geographical distribution of productive industries across the UK, and thus stark regional disparities that have persisted for decades. The average UK gross value added (the value that producers have added to their goods and services) sat at 35 in 2018, far below London’s 46.3; meanwhile, all of Wales fell below the UK average.[_]

The problem, however, wasn’t the ICT revolution itself or globalisation per se, but the lack of deliberate domestic policies designed to smooth the transitions.[_] Government reoriented economic policy, through privatisation and deregulation, to reinforce the transformation of the UK into a modern knowledge-based economy. Yet little consideration was given to the connection between this new source of wealth and the former industrial heartlands, with London and the South East prospering while deindustrialised communities in the North East and Wales were left exposed, cut off from reliable sources of productive, well-paid jobs. This lack of opportunity created the perception that the government is bad at industrial strategy and fed the rise of populists and nationalists in UK politics.

The economic, social and political legacy of accelerated deindustrialisation over the past 50 years has resulted in approaches to industrial strategy that typically look to the past or constrain the role of the state as it seeks to transform economies. These unsuccessful approaches include:

  • Fixing the pain of the past with a nostalgia for manufacturing. Despite manufacturing output rising by almost 50 per cent over the past 25 years,[_] these policies promise reindustrialisation as a means of undoing the actions of the past 50 years and restoring a community’s economic identity. They are often dressed up in the guise of the net-zero transition, or making supply chains less vulnerable.[_] Inevitably, however, they simply add costs to the taxpayer via subsidising jobs and end up making supply chains more vulnerable.

  • Presenting a false choice between manufacturing and services. This approach argues for developed economies to accelerate the pivot away from manufacturing by leaning into the shift towards services.[_] However, this ignores how important manufacturing is to the net-zero transition, national security and tech development. It also relies on narrow economic assessments of competitive advantage that fail to capture how AI-era technologies are revolutionising both manufacturing and services.

  • Prioritising a constrained view on the role of the state. This limits policy to a narrow focus on correcting market failures and means that the state is unable to be a platform for new innovations, technologies and entrepreneurship.

This legacy approach to industrial strategy is causing more harm than good to UK industry and productivity. The economy is changing, with technologies advancing quickly and upending old business models and employment patterns, as large language models enhance workforce productivity.

The government needs to take advantage of the technological revolution to reimagine and redefine how industrial strategy is set – and, in the process, address the issues of stagnant productivity and regional inequality to successfully level up the UK.

Figure 2

Traditional types of industrial strategy and how they interact

Source: OECD, “An Industrial Policy Framework for OECD Countries”


Chapter 5

The Government Needs to Get This Tech Revolution Right

While politics and policy remain consumed by a tech revolution that began 50 years ago, a new tech revolution is now underway. Conventional wisdom, however, remains sceptical of the transformational impact of this latest iteration.

Economists, for example, like to quip that they can see the impact of tech everywhere but in the productivity stats. This view reflects the traditional economic model, whereby output is a combination of capital and labour, and more technology leads to a more productive use of labour. According to this thinking, the UK’s productivity hasn’t risen despite recent advances in technology.

In fact, the UK’s problem is the lack of development and diffusion of tech from the frontier to the rest of the economy. This has three potential explanations.

The first is that new technologies only diffuse from the frontier at a slow pace;[_] economists call this trend the productivity J-curve. In the initial phases of new tech revolutions, the benefits of the technology are captured by a handful of firms, which tends to lower overall productivity. The tools to deploy new tech across the economy only become available over time, meaning a lack of tech diffusion creates a long tail of low-productivity companies. As far as many economists are concerned, the productivity of recent digital innovations will arrive as most economies are still in the “dip”.[_]

The second explanation is that it is getting harder to discover valuable new technologies. While new technology does boost productivity, the core problem is that the productivity of research and development (R&D) is slowing – and so productivity is also slowing.

The third tends to stress the fact that tech automates manufacturing, raising the costs of labour-intensive services such as health care. In a world of rapid technological progress, economies should expect the relative cost of manufactured goods such as cars and food to fall, while the relative cost of labour-intensive services such as health and child care should rise.[_]

Each of these views is based in both theory and real-world evidence, but they should be thought of as descriptions of the current state of the economy and policy rather than constraints on the future. And more critically, this conventional wisdom on the state of technology and the economy forgets to capture five important factors:

  1. New technologies create winner-takes-all dynamics. From Alfred Marshall[_] to Alan Krueger,[_] economists know that tech shifts strengthen winner-takes-all dynamics in markets, benefiting the few individuals or companies that can operate at scale and depressing wages and revenues for those that cannot. An economy based on technology and intangibles typically leads to greater market concentration and rising inequality,[_] and so will naturally drag on economic growth and productivity. Slow productivity growth is therefore a direct consequence of the UK’s approach to the tech revolution, not something that happens despite it: the signs of the tech revolution are actually everywhere, even in the relatively weak productivity stats.

  2. Policy can help accelerate the adoption of new technologies. Too often, economists assume that the shape of the productivity J-curve is determined by a path of technological development and adoption that the government cannot influence. But government does have agency over how it shapes and even accelerates both the creation and deployment of this process.[_]

  3. R&D productivity is accelerating on new frontiers.[_] R&D productivity suffers when funding systems favour established researchers on minimal advances, a process that is bureaucratic, slow and resource heavy. Conversely, productivity soars when new researchers leverage innovative technologies and funding methods. The UK, lacking in cutting-edge technologies and structural diversity, has an ossified, oligarchic research landscape that is unfriendly to young talent. This stagnation results from the government’s inability to emulate successful 20th-century lab strategies and utilise diverse talents. Significant reforms to the UK’s research infrastructure and government operations are necessary for improvement.

  4. Investing in hypercompetitive sectors generates local and national wealth. The UK needs another model to understand how technology could support economic growth. New tech accelerates competitiveness in wealth-creating, hypercompetitive tradeable sectors. Workers in these sectors then tend to “overpay” for untradeable local services. As UK economist Giles Wilkes wrote, “Hairdressers, lawyers, politicians and teachers are better off because of exogenous changes they are not directly responsible for. Economic progress is largely a game of free-riding.[_] What Wilkes calls “free-riding” is actually the wealth generated by hypercompetitive sectors effectively deploying frontier technologies. If the UK’s hypercompetitive sectors – including advanced manufacturing, biotech, digital creative industries and finance and fintech – aren’t successfully deploying new technologies, then the tech-driven engine of growth and productivity is broken.[_]

  5. The creative and disruptive impacts of AI need to be managed as best as possible. Several aspects of AI’s development pose challenges to conventional policy and governance approaches. The first arises from the speed of change, with AI developing at a rate that is taking even some of its pioneers by surprise. The second is the unpredictability of progress. For example, five years ago the consensus was that creative industries would be among the last to be automated, but generative-AI models have started to change that. AI is not being driven by an overarching theory: instead, it is developing primarily through tinkering and experimentation. This means that in this era of AI based on deep learning, creators do not know the full extent of its capabilities.
    The third is the expertise needed to understand and build AI: the experience required for global AI development is held by a small, highly sought-after pool of people, mostly based in private labs and definitely not in the Whitehall system. The fourth is the scope and scale of AI’s potential power, in that machines with the ability to outperform humans will have capabilities that cannot yet be imagined. Resulting from this is the fifth – and most socially significant – challenge: the rate and scale of change in the way that societies function and are arranged. The automation of cognitive labour poses a profound technological shift in how tasks are completed, knowledge is produced and information is communicated. If this comes to fruition, fundamental aspects of how society and our economy function will need to be adapted quickly.

Reimagining Industrial Strategy

The challenge for the UK and other economies is to create shared prosperity by accelerating the development of new technologies. For the UK, this will require the government to learn the lessons of past tech revolutions to set out how the AI revolution could support economic growth.

This model of economic growth requires a new approach to industrial strategy to accelerate the innovation, adoption and diffusion of AI. Central to this new approach is the recognition that AI is not merely a new technology but an upstream enabler of productivity, competitiveness and innovation in the UK’s wealth-generating and job-creating sectors.

An AI-era industrial strategy should aim to accelerate the technological transformation of the economy that is already underway, in order to generate economic value from AI-era technology faster and reap the rewards of higher productivity sooner. In short, the objective of industrial strategy should be to bend the J-curve: to shorten the productivity dip and hasten its productivity dividends.

An AI-era industrial strategy represents a significant departure from traditional approaches to industrial policy. Whereas conventional industrial strategies have often focused on supporting specific sectors or places, an AI-era approach recognises that the transformative potential of AI extends across the entire economy. In addition, traditional industrial policy instruments – such as production and employment subsidies, rigid tax incentives and trade protections – may have limited effectiveness in the context of the AI era, which is characterised by rapid change, uncertainty and complex feedback loops between technology, the economy and society.

Realising the full potential of AI requires a new set of policy instruments that embrace a more agile and adaptive approach to policymaking – one that is guided by experimentation, learning and iteration. This requires a coordinated and proactive approach, powered by a centre of government where science and technology expertise are deeply embedded.

Instead of “picking winners” or only backing specific sectors or places, the government must instead become a platform for the innovation, adoption and diffusion of productivity-enhancing AI-era technologies. This will involve the use of regulatory sandboxes, data trusts and disruptive-innovation labs to accelerate the development and deployment of AI-era technologies. And this will need to happen across a range of sectors, including biotech, advanced manufacturing, agtech and clean-energy systems. It may also require new forms of procurement and co-investment models that can share the risks and rewards of AI development and deployment across the economy.

Lastly, competition policy must be comfortable with AI-powered businesses from across the UK growing into giants of the world economy and becoming nodes in the global generation of economic value.


Chapter 6

The How of AI-era Industrial Strategy: Policy Instruments

An AI-era industrial strategy represents a significant departure from traditional approaches to industrial policy. As such, realising the full potential of AI also requires a new set of policy instruments that can address the unique challenges and opportunities presented by this technology.

Drawing from the specific recommendations in the New National Purpose series of papers produced by TBI on innovation, AI and biotech, the framework below provides a high-level blueprint for how the UK government can deliver industrial strategy in the AI era. The first two recommendations focus on the need to put science and technology at the heart of policymaking and the common upstream resources needed regardless of the downstream application of AI. The following three recommendations suggest measures that could be deployed as part of a sector- or technology-focused strategy.

1. Invest in Political Authority

As part of an accelerated AI-era industrial strategy, strong central political leadership is essential to drive rapid technological transformation and maintain a competitive edge in key industries. This requires a fundamental reorientation of government priorities, structures and decision-making processes to place science and technology at the heart of policymaking.

Key policy instruments under this category include:

  • Establish a central unit at the heart of government to drive the science-and-technology agenda. This unit should report directly to the prime minister but be empowered to set strategic priorities, coordinate cross-government activities and oversee the implementation of key policies and programmes. It should be staffed by a combination of policy experts, technologists and industry leaders, with a mandate to drive transformative change across all areas of government.

  • Create joint policy and delivery teams across science and technology, spanning key departments. These teams should be responsible for developing and implementing sector-specific technology strategies that align with the overall national vision. They should work closely with industry, academia and civil society to identify opportunities and challenges, and co-design policies and programmes that can accelerate innovation and adoption.

  • Appoint experts as specialist executive ministers in crucial technology areas. These ministers, across areas such as AI, biotechnology, quantum computing and clean energy, should have deep expertise in their respective fields and be empowered to drive strategic initiatives and investments. They should work closely with the central science-and-technology unit and the joint policy and delivery teams to ensure a coordinated and coherent approach across government.

  • Embed foresight, scenario planning and crisis-simulation capabilities within government. This would help to anticipate technological changes and build economic resilience; it could also involve the creation of a dedicated Centre for Strategic Futures or Ministry of the Future. This body would report directly to the prime minister and be tasked with scanning the horizon for emerging trends and disruptions, and working with departments to develop new long-term planning tools, adaptive strategies and contingency plans. It could also involve regular crisis simulations and wargaming exercises to test the government’s preparedness for different scenarios, such as economic and fiscal shocks, cyber-attacks, pandemics and technological accidents.

  • Invest in digital foundations. Data are critical to unlocking the benefits of AI. As it stands, huge amounts of public data are distributed – owned by different departments and bodies – but only partially connected, often in an ad-hoc manner. While the government has made admirable efforts to address this in core areas of public-service delivery, there is an opportunity to go further. Creating a shared data architecture is a huge lift but it is just as important as the physical infrastructure that has housed our public services over the past century. Building a single digital-ID system for all residents would unlock a range of public- and private-sector services. In Singapore, for example, the Singpass digital ID is used to access more than 700 private-sector services; in the UK, treating health data as an asset could positively contribute to both public health and the public finances, all while keeping patients’ data anonymous and secure.

2. Create a Platform for Growth by Unlocking Common Upstream Resources

An effective AI-era industrial strategy creates the underlying conditions and platforms that enable innovation to flourish across the entire economy. This means investing in the critical infrastructure that can be leveraged by multiple industries and applications, as well as removing barriers to the flow of knowledge, data, talent and capital.

Key policy instruments under this category include:

  • Streamline bureaucracy in research funding and management. This will give institutions greater autonomy and flexibility to pursue their own strategies and priorities. It could involve reducing the burden of grant applications and reporting, introducing more flexible funding arrangements such as core funding and portfolio approaches, and allowing institutions to retain a greater share of their intellectual property and commercialisation revenues. It could also involve creating a more supportive and agile regulatory environment for research and innovation, with faster approval processes and more scope for experimentation and risk-taking.

  • Reform research funding to prioritise high-risk, high-reward projects, support early-career researchers and reduce bureaucracy. This could involve setting aside a proportion of research budgets for blue-skies research and experimentation. It could also involve streamlining grant application and reporting processes, and providing more support for researchers to develop their ideas and collaborate with partners outside of academia.

  • Invest in critical digital infrastructure such as cloud computing, high-performance computing and fast, reliable connectivity. It would also be a priority to ensure that these resources are accessible and affordable for businesses and researchers across the country. This could involve partnering with industry to build and operate shared facilities and platforms, as well as providing targeted support and incentives for the adoption of advanced technologies such as 5G, the internet of things and edge computing. It could also result in the development of a national strategy for AI and data infrastructure, including the creation of foundational models and data sets that can be used by multiple sectors and applications.

  • Expand the availability of capital for deep-tech ventures. This would be done through a combination of public investment, co-investment funds and pension reforms, and could involve reforming R&D tax credits to account for AI-driven R&D. It could also involve increasing the scale and scope of existing schemes such as the British Business Bank (BBB) and the BBB’s Future Fund, as well as creating new vehicles for long-term investment such as consolidated pension funds. Another possibility would be working with industry and the financial sector to develop new financing models and instruments that are better suited to the needs of high-risk, high-reward innovation, such as venture debt, revenue-based financing and crowdfunding.

  • Attract global talent and investment to make the UK a leading hub for science and technology companies at all stages of their growth. This could involve reforming the visa system to make it easier for high-skilled workers and entrepreneurs to come to the UK, as well as providing targeted support and incentives for international businesses to establish R&D centres and headquarters in the country.

  • Address concerns over AI and the future of work by making a skills-and-training strategy part of industrial strategy. This would include a national programme of training and education in digital and innovation skills, to ensure that businesses and workers have the capabilities and confidence to adopt and use new technologies. This could feature a mix of online and in-person courses, workshops and mentoring, delivered through a network of universities, colleges and training providers. It could also include a focus on reskilling and upskilling workers in sectors and occupations that are most at risk of disruption from automation and AI, such as manufacturing, retail and administration.

3. Build a Focused Research Environment

Innovation lies at the heart of any successful industrial strategy, but the pace, scale and complexity of technological change in the AI era demands a new approach. An AI-era industrial strategy requires a more ambitious, agile and adaptive approach to innovation that can keep pace with the rapid development of new technologies and their convergence across multiple sectors.

Key policy instruments under this category include:

  • Establish a network of interdisciplinary research institutes focused on key technologies such as AI, biotechnology, quantum computing and clean energy. These institutes should bring together leading researchers from academia and industry to work on ambitious long-term projects that aim to push the boundaries of what is possible. They should be designed to foster a culture of collaboration, risk-taking and experimentation, with a focus on translating theoretical breakthroughs into practical applications with real-world impact.

  • Pioneer new institutional models for collaborative, interdisciplinary research that break down traditional silos and hierarchies. This could involve creating flat, agile research organisations with small, autonomous teams working on specific challenges – such as TBI’s recent recommendation for a Biodesign Lab – as well as investing in shared infrastructure and resources that can be accessed by multiple projects and partners. It could also involve new funding models that prioritise people and ideas over projects and institutions, such as fellowships, prizes and investigator-led grants.

  • Develop sectoral data trusts and frameworks to enable the secure and ethical sharing of data across organisations and industries. This could involve the creation of independent, not-for-profit entities to manage and govern data assets on behalf of their members, as well as developing common standards and protocols for data collection, storage and use. It could also include incentives and support for businesses and researchers to participate in data-sharing initiatives, such as innovation challenges, testbeds and sandboxes.

  • Launch mission-driven research programmes aligned with strategic technology priorities. For example: developing safe and ethical AI systems, creating sustainable biomanufacturing processes or building quantum-secured communication networks. These programmes should have clear, ambitious goals and timelines, and should be designed to catalyse collaboration and investment across multiple sectors and disciplines. They should also be accompanied by robust governance and accountability frameworks to ensure that they deliver tangible benefits for society as well as the economy.

  • Build on existing centres of excellence in areas where the UK has a comparative advantage, such as genomics, materials science and financial technology. At the same time, invest in new centres of excellence in emerging fields such as synthetic biology, neuromorphic computing and green hydrogen. These centres should act as hubs for research, innovation and skills development, and should be closely connected to regional and national innovation ecosystems.

4. Diffuse Technology From the Frontier to the Wider Economy

In an era where the pace of technological change is accelerating, one of the key challenges in any industrial strategy is ensuring that innovations developed at the cutting edge of R&D are quickly and effectively diffused throughout the wider economy.

Key policy instruments under this category include:

  • Create a programme of regional AI Trailblazers. This would match businesses in the most AI-suited sectors with AI and cloud platforms, to develop AI-powered solutions to their business problems. Drawing on an approach trialled in Singapore, the Trailblazers programme would act as a sandbox, with the aim of identifying and developing 100 generative-AI use cases across industry within 100 days; this would establish the UK as a leader in the responsible and safe deployment and adoption of impactful AI. It could be set up in leading UK cities such as Manchester, Birmingham, Glasgow and London, providing 100 small and medium-sized enterprises (SMEs) in each location with seamless access to Google, OpenAI and Facebook’s resources – AI platforms, compute power, pre-trained generative AI models and low-code developer tools – at no cost, for a fixed duration of up to three months. With access to these generative-AI toolsets, organisations can design and prototype their own generative AI solutions in a controlled and dedicated cloud-based environment.

  • Create regulatory sandboxes and testbeds to enable the rapid testing and iteration of new technologies and business models in real-world environments. These sandboxes should be designed to provide a safe and controlled space for experimentation, with reduced regulatory barriers and increased support for innovation. They should cover a range of sectors and applications – from financial services and health care to transport and energy – and should be open to both startups and established firms.

  • Establish a network of regional innovation clusters and hubs. This would bring together businesses, universities and research institutes to collaborate on shared challenges and opportunities. These clusters should be focused on specific sectors or technologies where the UK has a competitive advantage, such as life sciences, aerospace or fintech, and should provide access to specialist facilities, talent and funding. They should also be linked to national and international networks of expertise and support, such as the Catapult centres and the Knowledge Transfer Network.

5. Create Quicker Routes to Market

An AI-era industrial strategy would use demand from both the public and private sectors to stimulate AI-driven innovation.

Key policy instruments under this category include:

  • Establish an Advanced Procurement Agency to stimulate demand for innovative products and services, and to create lead markets for emerging technologies. This agency should have a mandate to identify and support strategic priorities, and to work with industry and the public sector to co-design and co-fund challenge-led procurement programmes. It should also have the flexibility to use a range of procurement methods, including pre-commercial procurement, innovation partnerships and outcome-based contracts, to support different stages of the innovation process.

  • Ensure economic regulation is designed to ease and even accelerate the route to market for new technologies. This could include restructuring the UK’s system of economic-sector regulation, with an cross-sectoral regulator able to ensure that innovation is addressed on a cross-sector basis, rather than sector by sector.[_] It could also include the creation of mutual recognition agreements for those instances when new technologies – such as drug therapies – are produced via international collaboration.

  • Reform the UK’s science translation and technology transfer system. This will better support the commercialisation and diffusion of research from universities and public research institutes. It could also include certain measures: streamlining the process of licensing and transferring IP, such as standard contracts and templates; providing more support and incentives for researchers to spin out companies and collaborate with industry; and increasing the transparency and accountability of technology transfer offices. Reforms to the patent system to reduce the costs and complexity of obtaining and enforcing patents could also feature, particularly for SMEs.


Chapter 7

Conclusion

AI is the most important technology of our generation.

The potential opportunities are vast: to change the shape of the state and the nature of science, and to augment the abilities of citizens. By the same token the risks are profound, and so the time to shape this technology positively is now.

For the UK, this task is urgent. Productivity and real wages have stagnated since 2008. AI is already delivering productivity improvements in areas such as coding, professional services and scientific discovery, and by some estimates has the potential to double economic output over the next 20 years.

Getting policy right on this issue is fundamental and could determine Britain’s future. While AI is already diffusing across our economy, the pace of progress is too slow and risks falling behind the speed of change in AI.

Reimagining industrial strategy for the AI era could transform the UK economy. Industrial strategy, however, is only one of the policy tools that is needed to prepare the UK for the AI era: the likes of public procurement, inward-investment regimes, workers’ rights and education systems will all need to be reformed and modernised. The UK’s public-sector institutions are not configured to deal with science and technology, particularly their exponential growth. It is absolutely vital that this changes.

Footnotes

  1. 1.

    https://twitter.com/jeegarkakkad/status/1553874615117774849?s=20&t=HEZKeRtbtFedfhNHMA%5F8FA

  2. 2.

    https://obamawhitehouse.archives.gov/sites/default/files/docs/hope%5Fand%5Fdreams%5F-%5Ffinal.pdf

  3. 3.

    https://www.imperial.ac.uk/news/234937/institutions-need-keep-with-changing-economy/

  4. 4.

    https://www.ted.com/talks/erik\_brynjolfsson\_the\_key\_to\_growth\_race\_with\_the\_machines/transcript

  5. 5.

    https://www.nature.com/articles/s41586-023-06735-9

  6. 6.

    https://news.uchicago.edu/story/human-aware-ai-helps-accelerate-scientific-discoveries-new-research-shows

  7. 7.

    https://sites.harvard.edu/uk-regional-growth/economics/

  8. 8.

    https://www.ons.gov.uk/visualisations/dvc1370/index.html

  9. 9.

    https://www.whitehouse.gov/briefing-room/speeches-remarks/2022/10/13/remarks-on-executing-a-modern-american-industrial-strategy-by-nec-director-brian-deese/

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    https://www.ons.gov.uk/economy/economicoutputandproductivity/output/timeseries/k22a/diop

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    https://www.whitehouse.gov/briefing-room/speeches-remarks/2023/04/27/remarks-by-national-security-advisor-jake-sullivan-on-renewing-american-economic-leadership-at-the-brookings-institution/

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    The UK’s Productivity Problem: Hub No Spokes (bankofengland.co.uk)

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    https://www.economicsobservatory.com/can-digital-innovation-including-ai-improve-productivity-growth

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    https://freethinkecon.wordpress.com/2023/09/28/a-blob-chart-way-of-dissecting-britains-prosperity-failure/

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    https://obamawhitehouse.archives.gov/sites/default/files/docs/hope%5Fand%5Fdreams%5F-%5Ffinal.pdf

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    Tech Is Splitting the U.S. Work Force in Two - The New York Times (nytimes.com)

  19. 19.

    https://www.economicsobservatory.com/can-digital-innovation-including-ai-improve-productivity-growth

  20. 20.

    https://jameswphillips.substack.com/p/ucl-talk-government-science-and-the

  21. 21.

    https://freethinkecon.wordpress.com/2023/09/28/a-blob-chart-way-of-dissecting-britains-prosperity-failure/

  22. 22.

    https://www.ft.com/content/892c6c1e-1d8f-11e9-a46f-08f9738d6b2b

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