AI’s clearest near-term macroeconomic effects are emerging through the demand that its capital investments create and strong equity market gains in a narrow subset of securities that has supported consumption growth through positive wealth effects. Spending on data centers, computing equipment, and power infrastructure supports activity before any widespread productivity improvement is established. The research reviewed here suggests that this investment cycle can lift growth and selected prices, even as its net employment effects remain uncertain.
GDP Growth Reflects Domestic Investment and Wealth Effects
AI-related spending has likely contributed 30 to 120bps to GDP growth over the year-to-date period.1 The lower estimate reflects direct AI spending, only, while the upper threshold throws in spending on software and AI-adjacent categories of expenditure. Corporate capital expenditure (capex) announcements cannot be translated dollar-for-dollar into U.S. GDP and the AI investment cycle has put upwards pressure on nominal prices of many electronics and metals, due to the infrastructure buildout, which show up in inflation and must be adjusted for in reporting of real GDP growth.
The lower end of the AI-contribution-to -GDP-growth calculation isolates the direct effects of AI spending and reflects technicalities associated with calculating GDP. The estimate ignores net exports and may underreport AI-related growth by up to 30bps, meaning the total potential contribution at the low end of estimates can be closer to 60bps.
Some large chip manufacturing companies produce chips abroad and sell them from those foreign locations, effectively underreporting the benefit of selling the chips in the domestic GDP figure.2 At the upper end of the estimated 120bps range, broader software spending, information processing equipment and non-residential structures are entirely included, which may be biased too high, as some of that spend is not directly AI-related. Software-related investments have been growing robustly for a decade. Another distinction is the contribution that AI is having from AI-related infrastructure development for data centers as contrasted with AI-technology-driven adoption. We can further attempt to decompose that growth from infrastructure at approximately 80 percent of that growth and about 20 percent from adoption of the technology.3
AI-related gains in the stock market have also supported consumption growth, which is the largest component of GDP at about 67 percent. In fact, about 70 percent of the S&P 500 Index gains over the past year stem from the outperformance of AI infrastructure providers.4 These wealth effects increase the propensity to spend, with contributions to real GDP growth ranging from 30bps to 40bps.5
The wealth effect has supported consumption growth of 3.8 percent in both 2Q26 and 3Q26 real GDP q/q SAAR (seasonally adjusted annualized rate), which exceeded the 3.3 percent growth in average hourly earnings over the same period, indicating that the upper-end consumer (the upper end of the K in the K-shaped economy) has been driving consumption growth. The wealth effect is a reversible channel, and consumers may have a higher elasticity to changing consumption on the downside of equity markets than to the upside, posing some risk to slower growth if stock market gains reverse.
AI-related inflation pressures on core PCE inflation are concentrated and difficult to measure, but are smaller relative to inflationary impacts from the Iran conflict and tariffs.
AI infrastructure has increased prices for computer memory, computing equipment, electricity, and industrial materials. Collectively, these channels currently add about 45bps to year-over-year (Y/Y) core PCE inflation. Roughly half of the current contribution reflects genuine price pressure, with the rest reflecting measurement issues, including the weighting of software and accessories and incomplete software quality adjustments.6 Though estimates vary, some research suggests that the inflationary impacts from the Iran conflict and tariffs account for about 100bp of inflation contribution, without which core PCE would be nearer the Federal Reserve’s (Fed’s) 2 percent target.7
Employment Gains and Hiring Pressures Coexist
In the near-term, the AI buildout has supported labor demand in construction, while AI adoption changes have reduced demand for some types of office and service work. Employment in construction categories exposed to data centers rose by roughly 317,000 additional jobs above the trendline since 2022.8 There has been modest evidence of employment pressure in marketing, graphic design, customer service, and some back-office technology occupations.9
Additionally, there are hiring headwinds concentrated among entry-level workers and occupations at greater risk of displacement. Evidence, to date, supports an uneven change in labor demand; but there is no broad support for definitive widespread net job destruction.10
Productivity Gains Depend on Adoption and Work Reorganization
AI adoption is expanding but remains uneven. AI adoption rates are increasing with approximately 24 percent of U.S. establishments utilizing AI in some capacity, with larger establishments leading adoption.11 The Fed cautions that adoption surveys do not measure how intensively firms use AI technology, limiting what headline adoption rates can tell us about either labor substitution or productivity.
Research from the Fed’s July monitoring note12 finds evidence of task-level productivity gains, but limited signs of an aggregate transformation. Integration costs and bottlenecks can prevent faster task completion from translating into proportional gains in output.
Potential Long-Term Effects of AI Growth
Longer-term employment effects noted in a May 2026 study13 from the Federal Reserve Bank of Chicago described meaningful but not apocalyptic job loss in rapid AI scenarios. The study found that workers in routine, codifiable tasks might face higher displacement risk, while high-skill workers who can leverage AI may see rising productivity and wages.
The International Monetary Fund (IMF) described a divergence between a low‑productivity path, where AI is adopted slowly, mainly as labor‑saving technology rather than creating new value, as differentiated from a high‑productivity path, where AI enables workers to raise output and living standards.14 The authors see the low-productivity path yielding higher inequality and weaker overall gains, and the high-productivity path encouraging higher degrees of work augmentation that generate larger advancements and rewards.
In the same study, researchers favored targeted measures like worker retraining and transition support. Without strong retraining and safety nets, inequality and regional dislocation could rise even if total employment remains high.
AI could raise GDP and productivity growth, although the timing and size of any gains remain uncertain. Under a scenario of rapid AI progress, the Chicago Fed study sees longer-term U.S. GDP growth potentially rising to around 4 percent, which is historically elevated and above trend.
Views offered by Fed Governor Michael Barr in 2026 held that productivity benefits could raise potential growth-of-output to a speed at which the economy can grow without generating inflation, while noting that AI-related capital investment could pressure rates higher.
Breckinridge Perspective
Breckinridge’s investment process includes a top-down process led by our Investment Committee and a bottom-up fundamental research component anchored by our Research team. The AI capital-spending boom is likely to require continued focus from both angles.
AI-related spending is certain to influence our top-down outlook, as AI spending influences growth, inflation, employment, and productivity. Each of these factors will shape Fed policy and inform our decisions about duration, sector allocations, and curve positioning.
The scale and breadth of the AI buildout is likely to impact many fixed income sectors, to a degree, from a credit perspective. Breckinridge anticipates it will selectively participate in bond offerings where we have high confidence in the fundamental strength of issuers and believe valuations are attractive, on a risk-adjusted basis.
Breckinridge’s High Quality Dividend Strategy may also be well positioned in the event of AI-driven stock market correction. The HQD strategy has a structural underweight to hyperscalers and the information/technology sectors, more broadly. Stock gains have been concentrated amongst a narrow subset of companies, largely in these sectors. HQD should exhibit lower volatility and more consistent returns in an AI correction scenario.15
[1] Estimates are drawn from analyses from sources including the Bloomberg Economics, Capital Economics, the Federal Reserve Bank of St. Louis, Goldman Sachs Research, and Morgan Stanley Research, as of October 28, 2026.
[2] How much is AI-related activity boosting GDP?, Capital Economics, October 13, 2026.
[3] AI Capex in Mid-Cycle Boom, Says Morgan Stanley’s Shalett, Bloomberg, October 28, 2026.
[4] “US Daily: Wealth Effects on Consumption Growth: AI Leads the Way” — Goldman Sachs, June 4, 2026, pp. 1–3.
[5] “US Economics Update: How much is AI-related activity boosting GDP?” — Capital Economics, October 13, 2026, p. 2.
[6] AI-driven inflation has peaked: Goldman Sachs, Yahoo!Finance, October 14, 2026.
[7] “US Economics Analyst: The Impact of AI Demand on Inflation” — Goldman Sachs, October 13, 2026, pp. 2 and 9.
[8] AI Adoption Tracker September 2026: Adoption Rises to 23.8%, Goldman Sachs Research, October 1, 2026.
[9] “AI Adoption Tracker — September 2026: Adoption Rises to 23.8%” — Goldman Sachs, October 1, 2026, p. 1, with additional evidence on pp. 12–13.
[10] “Global Economics Comment: Is AI Impacting Global Labor Markets?” — Goldman Sachs, August 19, 2026, pp. 4–6, particularly Exhibits 5–6 on p. 5.
[11] “AI Adoption Tracker — September 2026: Adoption Rises to 23.8%” — Goldman Sachs, October 1, 2026, p. 1 and p. 7, Exhibit 8
[12] The AI Buildout and the Economy: Publicly Available Data to Assess AI’s Impact, FEDS Notes, July 17, 2026.
[13] Forecasting the Economic Effects of AI, Ezra Karger, et al, Federal Reserve Bank of Chicago, May 2026. The forecasting study surveyed academic economists, AI company employees, policy researchers, and superforecasters.
[14] “The Macroeconomics of Artificial Intelligence,” Erik Brynjolfsson, Gabriel Unger, Finance & Development, December 2023.
[15] Strategies intended to identify, monitor, diversify or otherwise manage exposure to AI-related issuers, sectors or investments may not be effective. The growing integration of AI-related spending, financing, infrastructure, energy demand and revenues across the broader economy may create direct and indirect exposures that are difficult to identify, measure or mitigate. Correlations among issuers and sectors may increase during periods of market stress, and portfolio diversification may provide less protection than expected. Accordingly, efforts to manage AI concentration risk cannot eliminate the risk of loss or ensure that a portfolio will avoid adverse effects associated with a slowdown, repricing or disruption in AI-related investment.
Request #502640 (11/6/2026)
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