Goldman Sachs sees AI reshaping growth, inflation, credit, labor, currencies, and equity dispersion, creating new opportunities and risks for institutional allocators.
Goldman Sachs sees artificial intelligence supporting growth while reshaping inflation, credit supply, labor demand, currency dynamics, and the distribution of returns across markets.
Artificial intelligence has moved beyond a sector narrative and become a macroeconomic input. It is now influencing business investment, household wealth, inflation measurement, corporate borrowing, labor demand, and the relative performance of entire asset classes.
Goldman Sachs Asset Management expects global growth to slow during 2026 as elevated energy costs weigh on activity. Against that backdrop, United States growth is forecast near 2%, with AI investment supporting capital expenditure and contributing almost 0.5 percentage points to consumer spending through equity wealth effects. Emerging economies may receive an above trend growth impulse, while developed markets outside the United States remain below trend. Allocators must therefore assess where AI capital is being deployed, who owns the resulting assets, and how dependent consumption has become on sustained equity valuations.
Inflation complicates that allocation decision. Core inflation across the Group of Ten outside the United States has fallen to 2.1%, yet Goldman Sachs expects United States core inflation to approach 3% in December 2026 before moving closer to 2% during 2027. Tariffs, energy costs, and difficulties measuring AI related productivity all contribute to that divergence. Because much of the remaining inflation pressure originates from supply constraints, higher interest rates may be less effective than they would be against excessive demand. The resulting policy split supports greater selectivity across sovereign duration, yield curves, and currencies.
The equity market already reflects a sharp division between companies financing the AI buildout and businesses exposed to its competitive consequences. From January through July, the AI infrastructure basket tracked by Goldman Sachs rose to 148 from a starting level of 100, while the Standard and Poor’s 500 reached 109 and the AI at risk basket declined to 91. That dispersion creates opportunity for active managers, but it also increases concentration risk for portfolios whose technology exposure is defined only by index weight. Earnings quality, capital intensity, pricing power, and balance sheet resilience matter more than thematic classification.
Credit markets provide another transmission channel. Hyperscaler borrowing has represented between 16% and 23% of gross issuance across United States dollar investment grade bonds, high yield debt, and leveraged loans during 2026. Technology spreads have widened partly because markets must absorb that supply, even as underlying credit fundamentals remain reasonable at the senior end of the capital structure. Allocators gaining exposure to AI through debt should distinguish between borrowers funding durable contracted infrastructure and issuers relying on uncertain future utilization to justify present leverage.
Labor outcomes will determine whether the investment cycle produces enduring productivity or merely redistributes corporate profits. Goldman Sachs Research estimates that widespread AI adoption could raise labor productivity by about 15% over ten years and add $7 trillion to annual global output. Employers cited AI in 23% of the 444,000 United States job cuts recorded during the first half of 2026, although that figure includes both eliminated roles and displaced budgets. Goldman Sachs expects most occupations to be complemented rather than replaced, with technology, finance, healthcare, and services positioned to benefit while predictable task based roles face greater pressure.
Currency and real asset positioning also belong in the analysis. Rising United States rates and strong equity performance have supported the dollar, with Goldman Sachs expecting greater strength against developed market currencies than against emerging market peers supported by stronger equity performance and relatively attractive yields. At the same time, the firm’s forecasts favor gold and emerging market equities, suggesting that AI exposure need not be expressed solely through expensive United States technology shares.
The next allocation phase will depend on evidence rather than narrative. Investors should monitor the conversion of AI expenditure into revenue, the breadth of earnings growth, technology debt issuance, credit spread absorption, labor displacement, new job formation, energy demand, and central bank responses to supply driven inflation. AI can strengthen growth while increasing portfolio fragility. The distinction will be determined by adoption, financing discipline, and realized returns on capital.
