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Capital Market Assumptions: What AI Could Mean for Investors
AI’s adoption and advancement rates may counter declining productivity resulting from demographic shifts. Our confidence lies in AI’s longer term productivity boost versus shorter term adoption.
- Capital Market Assumptions
- Market & Investment Trends
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Key Points
What this is
AI’s asymmetrical profile is clear in our view, with far more potential for upside gain than downside loss.
Why it matters
Increasing productivity through AI is emerging as a critical factor for economic growth in the face of challenging demographic changes.
Where it's going
We believe AI adoption and gains will likely be more evident long term, as the technology is adopted and use cases are refined.
Every year, Northern Trust’s Capital Market Assumptions Working Group develops forward-looking, historically aware forecasts on global economic activity and financial market returns. We analyze historical relationships between asset classes and drivers of asset class returns, and we develop forward-looking investment themes to inform how those relationships and return drivers may change in the future.
Our 2025 Capital Market Assumptions 10-Year Outlook contains three themes that examine the trends we see affecting the markets and economy over the next decade, providing the foundation for our asset class outlooks. One of the three themes is AI-Enabled Productivity, and that is the focus of this article.
AI-Enabled Productivity
Increasing productivity through is emerging as a critical factor for economic growth in the face of challenging demographic changes. Historically, growth of the working age population has driven economic growth. However, fertility rates are declining (Exhibit 1), and governments are tightening their immigration policies for a variety of reasons, which likely will slow population growth. Further, life expectancies are increasing, resulting in a shrinking workforce as a percentage of total population in developed markets.
Artificial intelligence is the capability of computer systems or algorithms to imitate intelligent human behavior.
EXHIBIT 1: AI May Aid Productivity as Population Growth Slows
Historically, population growth has helped drive economic growth. As fertility rates decline, we expect AI to step in to increase productivity.
Source: United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition, data from 1950 to 2023. The replacement rate of 2.1 is the estimated number of births per woman required to maintain the population.
The potential impact of AI on productivity and economic growth is difficult to pin down and subject to great debate in academic and industry literature. While overall gains are acknowledged, the range of projections is wide. As examples, Daron Acemoglu, an economics professor at the Massachusetts Institute of Technology, argues that forecasting AI’s effect is hard to predict and requires speculation. Based on his framework, he sees a gain of 0.55% in total factor productivity over the next 10 years.1 In contrast, Goldman Sachs’ Joseph Briggs predicts a 9.2% gain in total factor productivity and 6.1% increase in economic growth over the next decade.2
Regardless of the wide range of estimates, AI’s asymmetrical profile is clear in our view, with far more potential for upside gain than downside loss. And while shorter term gains may come in spurts, or be obfuscated by other data, we believe AI adoption and gains will likely be more evident long term, as the technology is adopted and use cases are refined.
That said, AI could cause displacement in jobs and increase inequality. Potential AI productivity gains will likely differ across developed and developing economies due to the nature of existing work and respective states of technology. Within countries, impacts could also differ based on factors such as firm size, industry and adoption. Likewise, impacts on the labor market (acceleration, augmentation, automation) could vary.
As we look ahead to opportunities in an world, we see potential winners and losers. Infrastructure (data centers, energy infrastructure) seems attractive, with potential opportunities to capitalize on this theme in private markets. Meanwhile, some of the losers may be highly indebted companies lacking the sufficient access to credit necessary to invest enough in AI in order to remain competitive.
About the CMA Process
Every year, Northern Trust’s Capital Market Assumptions (CMA) Working Group gathers to develop long-term financial market forecasts. The team adheres to a forward-looking, historically aware approach. This involves understanding historical relationships between asset classes and the drivers of those asset class returns, but also debating how these relationships will evolve in the future.
Our forward-looking views are encapsulated in our annual list of CMA themes, which — combined with our quantitative analysis — guide our expectations for long-term asset class returns. The CMA return forecasts are combined with other portfolio construction tools (standard deviation, correlation, etc.) to annually review and/or update the recommended strategic asset allocations for all Northern Trust managed portfolios and multi-asset class products.
The CMA Working Group is composed of senior professionals from across Northern Trust globally, including top-down investment strategists, bottom-up research analysts and client-facing investment professionals.
1 The Simple Macroeconomics of AI” by Daron Acemoglu, Massachusetts Institute of Technology, April 5, 2024. Total factor productivity is a measure of product efficiency, calculated by dividing total production (output) by average costs (inputs).
2 Goldman Sachs Global Investment research, Top of Mind Issue 129: Gen AI: Too Much Spend, Too Little Benefit?, June 2024.
Main Point
AI-enabled productivity: More potential upside gain than downside loss
We see potential winners and losers. Infrastructure seems attractive, with potential opportunities to capitalize on this in private markets. Meanwhile, some of the losers may be highly indebted companies lacking the sufficient access to credit necessary to invest enough in AI in order to remain competitive.
Take a closer look at our Capital Market Assumptions
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Capital Market Assumption (CMA) model expected returns do not show actual performance and are for illustrative purposes only. They do not reflect actual trading, liquidity constraints, fees, expenses, taxes and other factors that could impact the future returns. Stated return expectations may differ from an investor’s actual result. The assumptions, views, techniques and forecasts noted are subject to change without notice.
CMA model expected returns are based on IPC forecasted returns and reflect Northern Trust’s Investment Policy Committee’s forward-looking annual capital market assumptions. The Capital Market Assumptions Working Group, a subset of IPC members, publishes its assumptions as a white paper report. Forecasted returns are annual returns (geometric basis). The model cannot account for the impact that economic, market and other factors may have on the implementation and ongoing management of an actual investment strategy. Model outcomes do not reflect actual trading, liquidity constraints, fees, expenses, taxes and other factors that could impact future returns. The model assumptions are passive only. References to expected returns are not promises or even estimates of actual returns an investor may achieve. The assumption, views, techniques and estimates set out are provided for illustrative purposes only. Forecasts of financial market trends that are based on current market conditions constitute the CMA Working Group judgment and are subject to change without notice. “Expected” or “alpha” return estimates are subject to uncertainty and error. The ability to achieve similar outcomes is subject to risk factors over which Northern Trust may have no or limited control.
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