Artificial intelligence — from investment boom to productivity test
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Article Type
Insights
Topic
Macro
Published
September 25, 2026
Read time
5 mins
Authors
Jacob Vijverberg, CFA
Jordy Hermanns, CFA
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Macroeconomic impact of AI

Artificial intelligence has moved decisively from a technology-sector theme to a macroeconomic and capital-markets theme. Its relevance now extends beyond software and semiconductors into capital expenditure, productivity expectations, electricity demand, infrastructure investment and market concentration. For professional investors, the key issue is no longer whether AI is a powerful technology, but whether the current investment cycle can translate into broad-based productivity gains and sustainable earnings growth.

 

The first visible macroeconomic impact is coming through investment rather than realised productivity. Spending on data centres, advanced semiconductors, cloud infrastructure, software platforms, power networks and cooling capacity is already supporting demand in the United States and selected parts of Asia. This creates a near-term growth impulse, but it also concentrates economic momentum in a relatively narrow set of sectors and companies. The United States currently benefits most from this cycle, reflecting the scale of its technology champions, deep capital markets, venture ecosystem and ability to finance large, long-duration investment projects.

 

Figure 1: Capex of hyperscalers

Source: Bloomberg, Aegon Asset Mangement (As of Aug 26)

 

Figure 2: Size of hyperscaler Capex  

 

 Source: Bloomberg, Aegon Asset Management (As of Aug-26)  

 

Over time, AI could help offset demographic pressure by raising labour productivity and automating routine cognitive tasks. This is particularly relevant for ageing economies where labour supply is becoming a structural constraint. However, the productivity benefits are not yet fully visible in aggregate economic data. At this stage, the AI boom remains more investment-led than productivity-led. The distinction matters for investors: an investment boom can support revenues and equity valuations in the short run, but only productivity diffusion can justify a durable improvement in potential growth, margins and fiscal sustainability.

 

Adoption is also likely to be uneven. Larger firms, highly digitalised sectors and economies with flexible capital markets may benefit earlier, while smaller firms, regulated industries and public-sector organisations may face slower implementation. Recent research suggests that AI is already creating some labour-market churn in AI-exposed activities, but the aggregate impact remains limited so far. Substitution effects appear to be only a modest drag on employment growth, while augmentation, new business models and the investment cycle partly offset the pressure.

 

The central uncertainty is whether AI becomes a general-purpose technology that improves the production of knowledge itself. If that happens, AI could differ from earlier waves of automation by accelerating research, software development, product design and decision-making across the economy. Yet the long-run growth impact remains highly uncertain. It depends on whether AI can overcome organisational bottlenecks, regulatory constraints and potential demand limitations. For investors, this makes AI both a powerful structural opportunity and a source of valuation risk if expectations run ahead of realised productivity gains.

 

Three scenarios

For investors, the central question is whether the current AI investment cycle evolves into a broader productivity regime change. The near-term macro impact is already visible through capital expenditure on semiconductors, data centres, cloud infrastructure and power capacity. The longer-term impact, however, will depend on whether AI diffuses across sectors, improves the production of knowledge and supports sustainable gains in margins, growth and fiscal capacity. Three broad scenarios help frame the range of possible outcomes.

 

Business-as-usual growth

In this scenario, AI improves specific tasks and supports selected sectors, but does not fundamentally alter the economy's long-term growth path. Productivity gains remain incremental, resulting in only a modest uplift in economic growth. Investment in AI infrastructure continues but remains concentrated in a relatively narrow set of applications and companies, limiting the need for a broad-based acceleration in capital expenditure. Inflation and real interest rates are only marginally affected, as labour remains the primary constraint on growth and aggregate demand does not materially strengthen. For equity investors, the benefits remain concentrated in a small group of technology companies, resulting in limited earnings breadth across the wider market.

 

Transformative growth

In this scenario, AI evolves into a true general-purpose technology that accelerates innovation, productivity and the creation of new knowledge. By automating parts of research, software development, product design and decision-making, AI raises potential growth significantly and becomes a major driver of economic expansion. The prospect of large productivity gains stimulates substantial investment in semiconductors, data centers, energy infrastructure and complementary technologies, resulting in a strong increase in AI-related capital expenditure. Faster growth and stronger investment demand put upward pressure on real interest rates, while the impact on inflation is mixed as stronger demand is partly offset by improved productive capacity. Unlike the business-as-usual scenario, the benefits spread well beyond technology companies. This combination of strong economic growth, high investment and expanding earnings breadth creates the most supportive backdrop for equity markets.

 

Delayed productivity

In this scenario, AI ultimately proves economically important, but the benefits arrive much more slowly than investors currently expect. Significant spending on implementation, workforce retraining, organisational restructuring and regulatory compliance delays productivity gains. As with earlier technologies, growth initially receives some support from AI-related investment, but aggregate productivity growth remains disappointing for several years. Investor expectations are gradually revised lower as the anticipated productivity dividend fails to materialize, resulting in weaker equity market performance. Lower equity valuations reduce household wealth and corporate confidence, leading to weaker consumption and reduced investment. The resulting slowdown puts downward pressure on real interest rates. While productivity eventually improves, earnings growth initially becomes more concentrated and uncertain.

 

Figure 3: AI's impact on macro-economy and markets  

 

Source: Aegon Asset Management (As of Aug-26)  

 

Our outlook

Our base case is one that sits much closer to the transformative growth scenario than to the traditional business-as-usual view. This makes AI the central productivity test in our outlook: the key question is whether the current investment boom can translate into broad-based, measurable productivity gains. We believe the answer is increasingly likely to be positive, as AI is displaying the characteristics of a general-purpose technology, similar to electricity or the internet, with applications spreading across all sectors of the economy rather than remaining confined to technology companies. Moreover, the speed of adoption has been exceptionally rapid, with AI becoming one of the fastest-diffusing technologies on record and evidence emerging that firms are already reporting productivity gains.

 

Examples of why AI could prove transformative include AI-assisted drug discovery, software agents that automate large parts of coding and business workflows, AI-enhanced engineering and design that accelerates innovation, and the widespread use of digital assistants that enable workers to produce more output without proportional increases in labour hours.

 

This article is part of the Long-Term Outlook, which was published in September 2026.

Disclosures

Authors

Jacob Vijverberg, CFA
Asset Allocation Lead
Jacob Vijverberg, CFA, is the asset allocation lead for the multi-asset team, responsible for both strategic and tactical asset allocation, and co-portfolio manager of the Aegon Global Diversified Income Fund. He also provides macro-economic analysis to internal and external stakeholders.  
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Jordy Hermanns, CFA
Portfolio Manager
Jordy Hermanns, CFA, is a portfolio manager in the multi-asset group and serves as an investment strategist in the asset allocation team. Jordy is responsible for strategic and tactical asset allocation decisions on the asset allocation team.  
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