How AI Is reshaping economic and strategic power
Reflections from discussion hosted by King’s College London and the London School of Economics
Executive summary
AI is evolving into critical economic infrastructure, making resilience, governance and strategic control increasingly important drivers of long-term value.
As access to frontier AI broadens, competitive advantage is likely to shift from owning the best models to deploying them more effectively through superior data, workflows and organisational execution.
AI assurance, governance and cybersecurity are emerging as attractive long-term investment themes as enterprise adoption and regulatory scrutiny increase.
Control over advanced AI capabilities is becoming a strategic issue for governments, with significant implications for industrial policy, supply chains and global competitiveness.
Investors should increasingly focus on the businesses that control the infrastructure, governance and ecosystems through which intelligence is created, distributed and trusted.
Our role is not only to manage capital, but to curate access, perspective and judgement from the most relevant conversations shaping markets and business. In mid-July, I attended “Whose AI is it anyway? Data, sovereignty and the geopolitics of intelligence,” a discussion jointly hosted by the King’s College London’s Institute for AI and the London School of Economics’ Data Science Institute.
Chaired by Professor Helen Margetts OBE, the expert panel brought together Ana Alania of the UK National Physical Laboratory, Dr Vasilios Mavroudis of King’s College London, Professor Mathias Koenig-Archibugi of the LSE and Priya Enefer, from the strategic advisory firm, Hakluyt. The discussion ranged from AI assurance and cyber security to regulation, sovereignty and international cooperation.
Rather than summarise the event, I have distilled what I believe are the most important implications for investors and business owners. Whilst the discussion itself was about AI, the core theme was power and control: Who controls AI models? Who can access which model? Who can verify the robustness of models? And who becomes dependent upon AI? Those questions may ultimately matter more than which company builds the next frontier model.
AI is becoming infrastructure
For much of the past three decades, technology has been viewed primarily as software: something organisations purchase, deploy and, if necessary, replace. Infrastructure is different. It creates a deeper level of dependency, becoming embedded within economies and critical organisational processes. As a consequence, resilience, interoperability, regulation and national interest become as important as innovation itself. Increasingly, AI appears to be making that transition.
Organisations are no longer simply purchasing software tools. They are beginning to rely on external AI systems to support research, analyse information, generate content, assist decision-making and automate knowledge work. In doing so, they are outsourcing part of their organisational judgement to entities they neither own nor control.
That changes the strategic conversation. Much of the debate around AI sovereignty focuses on ownership. Should countries build their own frontier models? For most nations, with the possible exception of the United States and China, complete technological self-sufficiency appears unrealistic. More importantly, it may not even be the right objective.
The more important question is resilience. Can organisations continue operating if access changes? Can they switch providers? Can they independently verify outputs? How exposed are they to pricing decisions, regulatory restrictions or geopolitical developments beyond their control?
Recent restrictions on access to frontier AI models illustrate the point (for example by the US). If access to advanced AI capability can be limited by decisions taken in another country, AI begins to resemble strategic infrastructure rather than commercial software. These are no longer software procurement questions – rather, they are questions of infrastructure resilience.
Competitive advantage is shifting from technology to execution
Much of today’s AI race is measured by model capability. Which company has built the smartest model? Which country has the greatest computing power? Which benchmark has just been surpassed?
Those questions obviously matter, but perhaps less than we think.
As frontier models improve and competition intensifies, access to highly capable AI is likely to become increasingly widespread. The gap between the leading models will remain important, but for most businesses it is unlikely to be the primary determinant of success. Instead, competitive advantage is likely to shift from access to technology towards the ability to deploy it effectively.
The organisations that create the greatest value may not be those with the most advanced AI. They may be those that redesign workflows, build proprietary data assets, strengthen governance, improve decision-making and integrate AI into day-to-day operations faster than their competitors. Execution, rather than technology, becomes the differentiator.
This has profound implications for boards. AI can no longer be viewed simply as another technology initiative delegated to the IT function. Decisions around model dependency, data governance, operational resilience, supplier concentration and accountability increasingly influence corporate strategy, capital allocation and enterprise risk.
In that sense, AI governance is rapidly becoming corporate governance.
Trust becomes an economic asset
The discussion repeatedly returned to a concept that receives far less attention than model performance: assurance. For AI to move beyond experimentation and into healthcare, financial services, defence and critical infrastructure, capability alone will not be sufficient. Organisations, regulators and customers will need confidence that AI systems are reliable, explainable and appropriately governed.
This is not a new challenge. Capital markets rely on audited financial statements. Aviation depends on certification and rigorous safety standards. Pharmaceutical companies operate within comprehensive testing and regulatory frameworks. Trust is not an optional extra - it is part of the infrastructure that allows these industries to function.
AI is likely to follow a similar path. As adoption accelerates, independent assurance, testing, auditability and governance are likely to become increasingly important sources of competitive advantage. Organisations that can demonstrate how their AI systems operate, how they are governed and how risks are managed may enjoy a meaningful commercial advantage over those that simply claim superior performance. That has important implications for investors.
Much of today’s attention is focused on model developers. Yet long-term value may also accrue to businesses providing AI assurance, cybersecurity, governance, compliance, testing and risk management. If trust becomes a prerequisite for large-scale adoption, the infrastructure that enables trust may prove just as valuable as the technology itself.
The geopolitical race is changing
Perhaps the most thought-provoking discussion of the evening was not about AI itself, but about power.
Public debate often portrays AI as a race to build the most capable frontier model. Increasingly, however, the more significant competition may be about controlling access to those models. Who receives the latest capabilities? Under what conditions? Can access be restricted, delayed or withdrawn altogether? These are no longer purely commercial questions. They are becoming matters of national strategy.
Recent restrictions on access to frontier AI models illustrate the point. Once governments can influence who benefits from the most advanced capabilities, AI begins to resemble other forms of strategic infrastructure. Semiconductors, compute capacity, export controls and international alliances become just as important as algorithmic breakthroughs.
This represents a subtle but important shift. Power increasingly lies not only in creating intelligence, but in deciding who can use it.
For investors, the implications extend well beyond the technology sector. AI should not be viewed simply as another software cycle, but as an emerging geopolitical force that will increasingly shape industrial policy, supply chains, capital allocation and international competitiveness.
A different investment framework
Every major technology cycle changes where value is created. The internet rewarded distribution. Cloud computing rewarded scale. AI may reward something different: the ability to organise, govern and deploy intelligence more effectively than everyone else.
History suggests that breakthrough technologies rarely create the greatest value at the point of invention. They create it through the infrastructure, standards and institutions that allow them to transform the wider economy.
AI is unlikely to be different. As frontier models become more capable and more widely available, competitive advantage is likely to migrate away from the models themselves. It will increasingly reside in the ecosystems built around them: proprietary data, workflow integration, governance, assurance, customer relationships and trust.
Seen through that lens, AI ceases to look like another software cycle. It begins to resemble a new layer of economic infrastructure. That single shift in perspective brings many of today’s debates into focus: sovereignty is about resilience, regulation is about trust, cybersecurity is about protecting cognitive infrastructure, and governance is about ensuring that intelligence can be deployed safely and consistently. These are not separate themes - they are different manifestations of the same structural change.
For business owners, the strategic question is no longer simply how to adopt AI. It is how to build an organisation that can deploy intelligence repeatedly, responsibly and at scale.
For investors, the question is no longer simply which company will build the best model. Instead, it is which companies will control the infrastructure through which intelligence flows. History suggests that those who control infrastructure often capture more enduring value than those who merely supply it. And that may prove to be one of the defining investment themes of the next decade.
Nick Perryman is Vice Chairman and Partner at Clarus Global Capital, and Chairman of its Investment Committee. Previously, he spent nearly two decades at UBS where was a Managing Director. He is co-author of the book, Leadership in Wealth: Mastering the Opportunities of Wealth in your Family, Firm and Society. He holds master's degrees from Durham and London universities, including in finance and organizational psychology, and is a doctoral researcher at Durham in financial services leadership, risk and governance. He is a Chartered Fellow of the Chartered Institute for Securities and Investment.
Important notice
This article is provided for general information, discussion and educational purposes only. It reflects the views of the author at the date of publication and is not intended to constitute, and should not be relied upon as, investment, financial, legal, tax, accounting or other professional advice. It does not constitute an offer, solicitation, recommendation or invitation to buy, sell or hold any investment, financial instrument or service, nor should it be regarded as a personal recommendation or as taking account of the objectives, financial circumstances or needs of any particular person.
