Beneath
the gold rush
the gold rush
The uneven economy of AI, from labour
and global power to energy and resources
and global power to energy and resources
Renée Friedman, PhD
Global Head of Research
EXANTE
Global Head of Research
EXANTE
Beneath
the gold rush
the gold rush
The uneven economy of AI, from labour
and global power to energy and resources
and global power to energy and resources
Renée Friedman, PhD
Global Head of Research
EXANTE
Global Head of Research
EXANTE
AI is the catalyst, not the conquering hero.
It does not remove demographic, geopolitical and environmental constraints. It relocates and intensifies them: productivity depends on skills, sovereignty depends on interdependence and decarbonisation depends on physical infrastructure.
“In this report, we examine how AI is reshaping each theme, how they interact and what their convergence may mean for the global economy and investors.”
It does not remove demographic, geopolitical and environmental constraints. It relocates and intensifies them: productivity depends on skills, sovereignty depends on interdependence and decarbonisation depends on physical infrastructure.
“In this report, we examine how AI is reshaping each theme, how they interact and what their convergence may mean for the global economy and investors.”
Renée Friedman, PhD
Global Head of Research, EXANTE
AI is the catalyst, not the conquering hero.
It does not remove demographic, geopolitical and environmental constraints. It relocates and intensifies them: productivity depends on skills, sovereignty depends on interdependence and decarbonisation depends on physical infrastructure.
“In this report, we examine how AI is reshaping each theme, how they interact and what their convergence may mean for the global economy and investors.”
It does not remove demographic, geopolitical and environmental constraints. It relocates and intensifies them: productivity depends on skills, sovereignty depends on interdependence and decarbonisation depends on physical infrastructure.
“In this report, we examine how AI is reshaping each theme, how they interact and what their convergence may mean for the global economy and investors.”
Renée Friedman, PhD
Global Head of Research, EXANTE
Beyond the investment story
When most people think about AI, they think first about its technology: software, semiconductor chips, robotics and automation. Yet, as we noted in our Alpha Vibes report, The Architecture of Disruption1, it is also a capital-intensive story, reshaping bond markets, power grids and equity leadership across the cycle.
Hyperscalers such as Meta, Alphabet, Microsoft, Amazon, Alibaba and Oracle are investing heavily in specialised chips, data centres and power infrastructure to capture what they see as a once-in-a-generation shift
in the global economy. Google, Meta, Microsoft and Amazon alone are
on track to invest more than $725bn in AI infrastructure in 2026.
Goldman Sachs2 forecasts $1tn in AI-related investment globally in 2026, including $581bn in the US.
Hyperscalers such as Meta, Alphabet, Microsoft, Amazon, Alibaba and Oracle are investing heavily in specialised chips, data centres and power infrastructure to capture what they see as a once-in-a-generation shift
in the global economy. Google, Meta, Microsoft and Amazon alone are
on track to invest more than $725bn in AI infrastructure in 2026.
Goldman Sachs2 forecasts $1tn in AI-related investment globally in 2026, including $581bn in the US.
$1tn
forecast global AI investment in 2026
$581bn
forecast US AI investment in 2026
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The significance of this spending extends well beyond the technology sector. As S&P3 notes, it is becoming one of the defining forces shaping
the global economic outlook, influencing manufacturing activity, trade flows, productivity expectations and labour-market planning.
AI also brings risks beyond the investment case. OpenAI’s Sam Altman
and SpaceX’s Elon Musk have supported a call4 by Anthropic’s Dario Amodei for a slowdown in the pace of AI development. He cites the loss of control over AI systems, their misuse in cyberattacks or bioterrorism and serious economic disruption.
The surge in AI-related corporate bond issuance also poses risks to bond and credit markets. Higher borrowing costs can filter through to individuals via inflation, tougher mortgage markets, higher credit-card rates and potentially weaker pension portfolios5.
The central question for investors, therefore, is not simply whether AI spending will generate returns above the cost of capital. It is whether AI can benefit all levels of society and improve human existence without causing irreversible environmental damage, weakening competition
and privacy or creating greater inequality.
the global economic outlook, influencing manufacturing activity, trade flows, productivity expectations and labour-market planning.
AI also brings risks beyond the investment case. OpenAI’s Sam Altman
and SpaceX’s Elon Musk have supported a call4 by Anthropic’s Dario Amodei for a slowdown in the pace of AI development. He cites the loss of control over AI systems, their misuse in cyberattacks or bioterrorism and serious economic disruption.
The surge in AI-related corporate bond issuance also poses risks to bond and credit markets. Higher borrowing costs can filter through to individuals via inflation, tougher mortgage markets, higher credit-card rates and potentially weaker pension portfolios5.
The central question for investors, therefore, is not simply whether AI spending will generate returns above the cost of capital. It is whether AI can benefit all levels of society and improve human existence without causing irreversible environmental damage, weakening competition
and privacy or creating greater inequality.
The main question is whether AI can benefit all levels of society and improve human existence without causing irreversible environmental damage, weakening competition and privacy or creating greater inequality
Three frontiers, one investment story
This series examines AI through three connected structural themes:
The labour frontier
How AI could help economies respond to ageing populations and labour shortages, improve productivity and support increasingly stretched healthcare systems.
The land grab
How control of compute, semiconductors, data, energy and critical minerals is reshaping sovereignty, alliances and competition for global dominance through hard and soft power.
Prospecting for gold
Whether AI can improve efficiency
and support climate solutions without intensifying pressure on power grids, water supplies, raw materials and emissions.
and support climate solutions without intensifying pressure on power grids, water supplies, raw materials and emissions.
Across all three, AI is a catalyst rather than a standalone solution. The reports examine how these forces interact and what their convergence could mean for the global economy and investors.
The labour frontier
Work, ageing and healthcare
In brief
- AI could offset the economic effects of ageing populations by improving productivity, expanding labour capacity and supporting overstretched healthcare systems.
- The evidence currently points more towards jobs being transformed than eliminated, although younger workers and routine entry-level roles face greater disruption.
- The gains will be uneven. Countries with strong digital infrastructure, skilled workforces and adaptable institutions are best placed to benefit.
Introduction
The evidence presented points in two directions at once, and the gap between them is where the investment questions sit. Investors worry about the impact AI will have on workers, productivity and company costs, as well as how deeply it will become integrated in day-to-day life. Will it really make the world more efficient? What about all the people it may replace? Could it destroy the economy?
History suggests that major technological advances have generally produced positive economic and social effects. One mechanism is the Jevons Paradox: when technology makes a resource cheaper or easier to use, demand for it tends to increase. Torsten Slok6 argues that a similar Jevons Employment Effect could shape AI’s impact on work. If AI makes workers more efficient and reduces the cost of completing tasks, companies may respond by expanding output. The resulting growth could increase, rather than reduce, overall headcount.
That outcome is not guaranteed. Regulation or other bottlenecks may prevent businesses from expanding enough to create new jobs. Where AI can replace human labour or intellectual input almost entirely, demand for workers may not recover at all. Excessive automation could therefore widen inequality and depress wages, particularly for people at the lower end of the educational and skills spectrum.
What is observable now is a softening entry-level jobs market and a wage premium for AI skills. What remains speculative is whether AI will deliver the productivity gains needed to justify the capital being spent. OECD modelling puts the potential gain at 0.4 to 1.3 percentage points a year, but that range assumes reskilling, organisational change and institutions that adapt. None of those is a given, and ageing societies tend to be weaker on all three.
The binding constraint, then, may not be the technology itself, but the human and institutional capacity to absorb it. Regulation may be the single biggest factor accelerating or constraining its spread. Meanwhile, the costs of the transition are already falling on identifiable groups: graduates entering the workforce, people in routine white-collar roles and low-income economies where diffusion has barely begun. The benefits are more dispersed and remain further out. That leaves two questions running through the paper: what would need to appear in the data before the productivity case is proven, and what would count as evidence that it has failed?
History suggests that major technological advances have generally produced positive economic and social effects. One mechanism is the Jevons Paradox: when technology makes a resource cheaper or easier to use, demand for it tends to increase. Torsten Slok6 argues that a similar Jevons Employment Effect could shape AI’s impact on work. If AI makes workers more efficient and reduces the cost of completing tasks, companies may respond by expanding output. The resulting growth could increase, rather than reduce, overall headcount.
That outcome is not guaranteed. Regulation or other bottlenecks may prevent businesses from expanding enough to create new jobs. Where AI can replace human labour or intellectual input almost entirely, demand for workers may not recover at all. Excessive automation could therefore widen inequality and depress wages, particularly for people at the lower end of the educational and skills spectrum.
What is observable now is a softening entry-level jobs market and a wage premium for AI skills. What remains speculative is whether AI will deliver the productivity gains needed to justify the capital being spent. OECD modelling puts the potential gain at 0.4 to 1.3 percentage points a year, but that range assumes reskilling, organisational change and institutions that adapt. None of those is a given, and ageing societies tend to be weaker on all three.
The binding constraint, then, may not be the technology itself, but the human and institutional capacity to absorb it. Regulation may be the single biggest factor accelerating or constraining its spread. Meanwhile, the costs of the transition are already falling on identifiable groups: graduates entering the workforce, people in routine white-collar roles and low-income economies where diffusion has barely begun. The benefits are more dispersed and remain further out. That leaves two questions running through the paper: what would need to appear in the data before the productivity case is proven, and what would count as evidence that it has failed?
Volatility rising: balancing market risks with opportunities
War upends the global macroeconomic environment
The macroeconomic backdrop heading into 2026 was characterised by competing forces: an expectation that interest rates globally would continue to normalise as inflation in most major economies, barring Japan, continued to fall, providing support to equity markets as bond markets steepened and the dollar continued to weaken. Earnings were expected to remain resilient and fiscal expansion was expected to support risk assets. Risks included persistent geopolitical tensions between Ukraine and Russia, tariff uncertainty, and uneven regional growth trajectories.
The US-Israeli-led war with Iran has, as noted by Dambiso Moyo, disrupted transit through the Strait of Hormuz, which accounts for approximately 20% of the world’s daily oil demand, creating the largest oil-supply shock in the history of the global oil market. This disruption has severely tested equity, bond, commodity and FX markets, forcing investors to reconsider the impact on inflation, the pace of monetary policy changes, supply chain interruptions, and currency exposures. GCC issuers, however, still have strong external balances, substantial foreign reserves and sizable sovereign wealth assets that provide an important cushion against volatility.
Geopolitical realignments
and breakdowns
and breakdowns
The war with Iran highlights a permanent change in geopolitical alignments, showing a real-time weakening of international coordination and declining effectiveness of multilateral institutions. The US may partially withdraw from NATO due to demands on its allies to participate in freeing the Strait of Hormuz. GCC countries, facing immediate existential threats, have been forced into closer alignment with the US and Israel, shifting away from their previous strategy of neutrality. The Middle East Council on Global Affairs has identified that the GCC’s challenge is to design a security posture that preserves autonomy while reducing exposure to both Iranian coercion and unwanted strategic entanglement. Given the difficulties in negotiating for needed technologies such as drones, the GCC states are now more likely to move towards building their own deterrence capabilities and aim for greater defence independence and accelerated indigenous defence industrialisation. We will also likely see, as noted by Yara Aziz, Senior Economist at OMFIF, greater co-operation on maritime security, energy infrastructure protection and trade logistics to improve resilience to future disruptions.
Geopolitical volatility is also increasing geoeconomic fragmentation, reshaping cross-border trade and investment flows and complicating global operational models. This has led to the emergence of what Deloitte has called “hybrid risks” that cut across financial, operational, technological, and geopolitical domains. As a result, some countries are reducing reliance on cross-border AI, data and technology stacks to strengthen supply chain resilience, which could complicate global firms’ efforts to maintain integrated resilience plans.
Geopolitical volatility is also increasing geoeconomic fragmentation, reshaping cross-border trade and investment flows and complicating global operational models. This has led to the emergence of what Deloitte has called “hybrid risks” that cut across financial, operational, technological, and geopolitical domains. As a result, some countries are reducing reliance on cross-border AI, data and technology stacks to strengthen supply chain resilience, which could complicate global firms’ efforts to maintain integrated resilience plans.
Overcoming structural pressures to achieve AUM growth
Scale matters
According to BCG, the global asset management industry reached a record $128 trillion in assets under management (AUM) in 2024, rising 12% from the previous year. As noted by Accenture, total AUM at the world’s 500 largest asset managers rose to $139.9 trillion at the end of 2024. However, global AUM growth is, as suggested by Moody’s, increasingly concentrated at the top, with the largest firms capturing a disproportionate market share. The top 20 asset managers controlled around 47% of global AUM in 2024.
Asset managers face structural fee compression due to the proliferation of passive investment products and rising investor cost-awareness. According to the Morningstar Active/Passive Barometer 2025, passive funds charge up to 60% lower fees than actively managed funds. When combined with increasing compliance costs for greater transparency and reporting, margins are severely compressed.
Identifying the profitability gap. Oliver Wyman noted that fund managers with over $2 trillion in assets have average margins of roughly 45%; those with less than $500 billion have 36%. Mid-sized firms (the “Valley of Death”) have average margins of 26%. It seems these middle sized management firms tend to compete in the same way as the largest management firms.
Asset managers face structural fee compression due to the proliferation of passive investment products and rising investor cost-awareness. According to the Morningstar Active/Passive Barometer 2025, passive funds charge up to 60% lower fees than actively managed funds. When combined with increasing compliance costs for greater transparency and reporting, margins are severely compressed.
Identifying the profitability gap. Oliver Wyman noted that fund managers with over $2 trillion in assets have average margins of roughly 45%; those with less than $500 billion have 36%. Mid-sized firms (the “Valley of Death”) have average margins of 26%. It seems these middle sized management firms tend to compete in the same way as the largest management firms.
They are too diverse and often too large to benefit from simpler operating models, but not large enough to operate at scale. They cannot easily acquire competitors and therefore may have to become more focussed asset or sector specialists.
The shift to private markets: Asset managers not only have to justify their fee structures in public markets, but also compete with private markets and alternative asset managers to scale model portfolios effectively and efficiently. There is a growing convergence across public and private markets and a blurring of wealth and asset management as well as product and distribution modes. According to Moody’s Global Asset Management 2026 outlook, private markets are likely to generate more than half the asset management industry’s total revenue by 2030, because they currently produce about four times as much profit per $1 billion in assets under management as do traditional managers. This means that in a higher for longer rate environment, traditional asset management firms with origination targets will become more challenged than those alternative providers who may have greater flexibility around volume targets and collateral types.
The shift to private markets: Asset managers not only have to justify their fee structures in public markets, but also compete with private markets and alternative asset managers to scale model portfolios effectively and efficiently. There is a growing convergence across public and private markets and a blurring of wealth and asset management as well as product and distribution modes. According to Moody’s Global Asset Management 2026 outlook, private markets are likely to generate more than half the asset management industry’s total revenue by 2030, because they currently produce about four times as much profit per $1 billion in assets under management as do traditional managers. This means that in a higher for longer rate environment, traditional asset management firms with origination targets will become more challenged than those alternative providers who may have greater flexibility around volume targets and collateral types.
Combat passive strategies
The rise of passive strategies, fuelled by the failure of many active managers to deliver long-term outperformance, is a fundamental challenge leading to fee compression. Asset managers must shift from a focus on pure beta to providing specialised, high-conviction alpha, leveraging AI to improve portfolio personalisation. A strategic divergence is emerging between large diversified platforms aiming for scale and highly specialised boutiques.
Specialisation vs scale. If an asset manager cannot achieve scale, specialisation is often preferable. McKinsey research indicates that specialist buyout funds generate better returns (17% pooled IRRs) than their generalist peers (13% pooled IRRs), underpinned by a focus on operational value creation.
The rise of ETFs. As observed by Deloitte in its 2026 Investment Management Outlook, customer preferences and regulatory change are contributing to firms transforming product lines from mutual funds to ETFs, with over $60 billion in assets making the transition.
Specialisation vs scale. If an asset manager cannot achieve scale, specialisation is often preferable. McKinsey research indicates that specialist buyout funds generate better returns (17% pooled IRRs) than their generalist peers (13% pooled IRRs), underpinned by a focus on operational value creation.
The rise of ETFs. As observed by Deloitte in its 2026 Investment Management Outlook, customer preferences and regulatory change are contributing to firms transforming product lines from mutual funds to ETFs, with over $60 billion in assets making the transition.
Consolidation and M&A
The consolidation wave accelerating through the industry is both a strategic risk for those left behind and an opportunity for those building scale. Asset management firms have been targeting firms that provide entry into the expanding private market space, into real assets or that have successfully incorporated advanced technologies that provide demonstrated ROI with relevant efficiency gains and cost reductions.
As highlighted by McKinsey in its Global Private Markets Report, total M&A deal value involving the top 100 alternative asset managers acquiring others reached its highest value since 2006 in 2025 (approximately $34 billion, almost double the $18 billion recorded in 2024).
As highlighted by McKinsey in its Global Private Markets Report, total M&A deal value involving the top 100 alternative asset managers acquiring others reached its highest value since 2006 in 2025 (approximately $34 billion, almost double the $18 billion recorded in 2024).
Wealth and asset management players continue to consolidate, with 156 deals worth a total of $113 billion in 2025 alone.
M&A is shifting towards capability-led deals that strengthen expertise in alternative assets. McKinsey found that managers are targeting firms that provide an edge in private markets, real assets, or advanced technology.
The use of generative and agentic AI is expected to accelerate M&A by making it easier to quickly integrate acquisitions and capture synergies.
M&A is shifting towards capability-led deals that strengthen expertise in alternative assets. McKinsey found that managers are targeting firms that provide an edge in private markets, real assets, or advanced technology.
The use of generative and agentic AI is expected to accelerate M&A by making it easier to quickly integrate acquisitions and capture synergies.
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While every effort has been made to verify the accuracy of this information, EXT Ltd. (hereafter known as “EXANTE”) cannot accept any responsibility or liability for reliance by any person on this publication or any of the information, opinions, or conclusions contained in this publication. The findings and views expressed in this publication do not necessarily reflect the views of EXANTE. Any action taken upon the information contained in this publication is strictly at your own risk. EXANTE will not be liable for any loss or damage in connection with this publication.