The Intelligence Dividend: What AI Means for Portfolios

By: Laura Cooper, Nuveen

As AI reshapes the macroeconomic landscape, pension fund leaders should understand how this investment cycle is influencing inflation, employment, and fixed income markets. This research outlines practical portfolio positioning strategies that can help public pension plans navigate uncertainty and protect long-term asset stability.

The AI investment boom is already one of the largest capital cycles in history. But its impact on economic growth, inflation, and employment is less certain than the scale of spending implies.

The Productivity Payoff — When and How Much?

Since 2024, 64 cents of every dollar of U.S. GDP growth has been attributable to tech spending.¹ Hardware investment has risen from nearly 2% of U.S. GDP to more than 3%, representing hundreds of billions of dollars.² This cycle has already seen productivity growth above 2%, ahead of last cycle’s 1.5% trend, but that largely reflects post-pandemic labor market dynamics rather than AI.³ The AI boost has yet to arrive, given that more than three in four U.S. businesses have yet to incorporate AI.⁴

That gap between investment and deployment explains why expectations of AI’s productivity impact vary so widely. The most conservative estimates put AI’s current contribution to total factor productivity at close to zero. More optimistic projections from the OECD suggest annual labor productivity gains of up to 1.3 percentage points for G7 economies with ‘high AI exposure.’⁵ Both can be true at different points in time. The internet’s productivity payoff took the better part of a decade to show up in national accounts. There is little reason to think AI will be faster as the organizational changes required are likely more complex.

A Complex Inflation Picture

What makes the inflation outcome particularly difficult to forecast is that AI is exerting pressure in multiple directions at once. The clearest empirical signal so far cuts against the consensus that AI is disinflationary. A newly created quarterly U.S. AI-intensity index finds that AI has already made a positive and rising contribution to U.S. inflation, with the most AI-exposed sectors tending to show the highest inflation rates.⁶ The steep costs of the AI buildout are arriving faster than the productivity gains that are supposed to offset them.

Those costs are most visible in energy. U.S. power inflation ran at 6.9% year-on-year through December 2025, more than double the headline PCE gauge.⁷ Consumer electricity inflation is likely to remain near 6% through 2027, while data center demand is set to nearly double from its current approximately 4% share of total U.S. electricity by 2030.⁸

As AI adoption broadens and output per worker rises, the resulting fall in unit labor costs could exert meaningful downward pressure on services inflation. But a June 2026 World Economic Forum survey finds economists now expect AI-driven productivity gains to take at least another two years to materialize across most sectors.⁹

Exposed, But Not Displaced, Workers

The employment picture follows a similar pattern. Significant pressure is building, but only limited disruption is visible in the data so far. The IMF estimates that close to 40% of global employment is exposed to AI, and roughly 4 of 5 U.S. workers have at least 10% of their tasks exposed to large language model capabilities.¹⁰

And yet since the introduction of generative AI, economy-wide job losses and wage declines have not materialized. General-purpose technologies have historically restructured work before they reduce it, sometimes by a decade or more, and AI appears to be following that pattern.¹¹

Portfolio Positioning When the AI Payoff Has Yet to Arrive

The energy data and the capex financing pipeline explain why AI is not a straightforward disinflationary story. For credit investors, the phase of the AI investment cycle matters more than broad sector exposure. Infrastructure credit is backed by contracted cash flows and long-duration demand, while spread pricing among AI-exposed incumbents may not reflect the disruption risk embedded in those names.

Two main positioning themes stand out. First, despite the recent move higher in longer-maturity yields, caution on duration exposure across fixed income markets is warranted. There is still potential upside to rates from the near-term, inflationary impact of AI investment. Second, a modestly risk-on stance with careful security selection within below-investment grade credit is recommended. Avoiding companies and sectors exposed to disruption is crucial, but income potential remains substantial in markets such as high yield bonds, senior loans, and emerging markets where active managers can navigate the AI crosscurrents and select potential winners.

Laura Cooper is Managing Director, Head of Macro Credit and Global Investment Strategist at Nuveen, where she provides directional guidance to internal portfolio managers and strategic insights to clients, helping to shape macro and top-down investment views. Prior to joining Nuveen, Laura led a team of multi-asset strategists at BlackRock dedicated to providing macro insights and tactical investment research.

Previously, Laura was the European macro strategist at Bloomberg and served as Director and Head of FX Strategy at RBC Wealth Management, shaping the global house view for G10 currencies and rates. She began her career as an economist with RBC Capital Markets.

Laura holds a Master of Science in Economics from the London School of Economics. Her research has been featured in The Economist and the Financial Times.

Endnotes:

1. BCA, Bloomberg, Nuveen estimates, June 2026
2. Bureau of Economic Analysis, May 2026
3. Bureau of Economic Analysis, May 2026
4. Census Bureau, May 2026
5. Filippucci et al., OECD/SUERF Policy Brief, 2025
6. Abo-Zaid, SSRN Working Paper 6529799, April 2026
7. Bloomberg
8. Nuveen estimates, 2025
9. WEF Survey, June 2026
10. IMF Working Paper WP/25/76, 2025; Eloundou et al, 2024
11. IMF Working paper WP/25/76, 2025

Disclosures: All market and economic data from Bloomberg, FactSet and Morningstar.

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