U.S. AI investment could reach an annualized $1.4 trillion by the end of 2027, according to Goldman Sachs’ latest forecast. The financial institution expects spending on artificial intelligence-related hardware and intellectual property to exceed $900 billion by the end of 2026, with further growth projected through 2027. The outlook identifies AI as a major contributor to U.S. business investment, with implications for semiconductor manufacturing, data center infrastructure, and corporate financing.
Key Takeaways
- Goldman Sachs forecasts annualized U.S. AI-related spending could reach $1.4 trillion by the end of 2027
- Spending on AI hardware and intellectual property could exceed $900 billion by the end of 2026
- AI-related investment could reach an annualized level equivalent to nearly 4% of U.S. economic output by the end of 2027
- Goldman Sachs projects U.S. business investment growth of approximately 8% on a quarterly basis in 2027, compared with a consensus estimate of 4.2%
- Semiconductor manufacturing, data center infrastructure, and corporate financing are among the areas associated with the spending outlook
Goldman Sachs Projects Higher U.S. AI Investment Through 2027
Goldman Sachs expects artificial intelligence to remain a major contributor to U.S. business investment growth in 2027, supported by spending on computing equipment, infrastructure, and intellectual property.
The institution forecasts that annualized AI-related expenditure will surpass $900 billion by the end of 2026 before reaching approximately $1.4 trillion by the end of 2027.
The projected increase reflects expectations that companies will continue allocating capital toward the infrastructure required to develop and operate artificial intelligence systems. These expenditures include computing hardware, supporting technology, and intellectual property associated with AI applications.
At the projected 2027 level, annualized spending would be equivalent to nearly 4% of U.S. economic output. The comparison illustrates the potential scale of AI expenditure relative to the broader economy, although the figure remains a forecast rather than a confirmed spending outcome.
Goldman Sachs also expects U.S. business investment growth to reach approximately 8% on a quarterly basis in 2027, compared with a consensus forecast of 4.2%.
The difference reflects expectations that AI-related capital spending will contribute substantially to business investment growth. However, the outlook remains sensitive to financing conditions, infrastructure development schedules, and corporate spending decisions.
AI Hardware and Intellectual Property Drive Spending Projections
The $1.4 trillion estimate covers two principal categories: artificial intelligence-related hardware and intellectual property.
Hardware includes computing equipment used to develop, train, and operate AI systems. These technologies require substantial processing capacity, particularly for applications involving large datasets and complex computational tasks.
Semiconductors and related computing systems are central to these requirements. Companies developing AI infrastructure must purchase or obtain access to processors, memory, and other hardware capable of supporting demanding workloads.
Intellectual property represents another component of the forecast. This category includes software and other intellectual assets associated with developing and deploying artificial intelligence technologies.
The distinction matters because AI investment extends beyond purchasing physical equipment. Businesses also incur costs associated with developing, acquiring, and maintaining the technology needed to operate AI systems.
For companies evaluating AI expenditure, these categories involve different financial considerations. Hardware investment may require substantial upfront spending, while software and intellectual property can involve development expenses, licensing arrangements, and ongoing costs.
Goldman Sachs’ forecast combines these expenditure categories to estimate the potential scale of AI-related investment through 2027.
AI Investment Could Support U.S. Business and GDP Growth
Goldman Sachs expects AI-related expenditure to contribute significantly to U.S. capital investment growth in 2027.
Including semiconductor investment, the institution estimates that AI-related spending could add approximately 5.3 percentage points to business investment growth and 0.6 percentage points to U.S. gross domestic product growth.
Excluding semiconductor investment, the projected contributions decline to 4.3 percentage points for business investment growth and 0.2 percentage points for GDP growth.
The distinction reflects differences in how equipment spending is captured in economic statistics. Goldman Sachs has identified limitations in the measurement of semiconductor investment, while imported information-processing equipment does not contribute directly to domestic GDP.
These estimates indicate that substantial AI-related expenditure does not necessarily translate into an equivalent increase in measured U.S. economic output.
Business investment contributes to GDP through spending on equipment, structures, and intellectual property products. However, the domestic economic effect depends partly on where equipment is manufactured and how investments are classified in national accounts.
For business leaders, the projections provide context for technology budgets and capital expenditure planning. They also highlight the importance of distinguishing between spending commitments and their eventual contribution to economic growth.
The forecast depends on whether companies proceed with planned investments and whether the underlying infrastructure becomes operational within anticipated timelines.
Semiconductor and Data Center Infrastructure Attract Capital Spending
Semiconductors and data centers are closely connected to the anticipated expansion in U.S. AI investment.
Artificial intelligence applications require processors, memory, and supporting computing systems capable of handling complex workloads. Businesses developing AI infrastructure must also secure physical facilities, electricity, cooling systems, and network capacity.
Semiconductor manufacturing projects illustrate the capital requirements associated with expanding AI hardware production.
One example is SK hynix’s Indiana AI memory facility, which involves more than $4 billion in planned investment. The West Lafayette project focuses on advanced packaging for high-bandwidth memory used in AI applications, with mass production scheduled for the second half of 2029.
Such projects demonstrate how AI-related capital expenditure can extend across semiconductor manufacturing, construction, and supporting infrastructure.
Data centers represent another component of the spending outlook. These facilities house computing equipment and provide the processing, storage, and networking capacity required for AI operations.
Their development also creates demand for electricity infrastructure and systems that support the operation of high-density computing equipment.
However, infrastructure spending depends on construction schedules, equipment availability, financing, and access to sufficient power capacity. These requirements can influence how quickly companies expand operations.
Goldman Sachs’ projected increase in AI spending therefore involves multiple sectors rather than companies developing AI software alone.
Corporate Financing and Capital Allocation Remain Important
The projected scale of AI-related expenditure creates financial considerations for businesses expanding their technology infrastructure.
Companies undertaking major AI projects must evaluate upfront costs, ongoing operating expenses, financing arrangements, and expected financial returns.
Goldman Sachs’ outlook comes amid concerns about higher borrowing costs and increased scrutiny of AI-related capital expenditure. These considerations are particularly relevant for companies financing infrastructure expansion through debt.
Borrowing can support large technology projects, but it introduces repayment obligations and interest expenses that affect overall project costs.
Finance executives are also taking a greater role in corporate AI investment decisions, including reviewing implementation costs, operating requirements, and expected returns before approving spending.
These evaluations may involve coordination among finance departments, technology executives, and operational teams.
For companies purchasing AI infrastructure, capital allocation decisions often include comparing equipment costs with alternatives such as cloud-based computing services. Software licensing, system integration, cybersecurity, and maintenance can also affect total expenditure.
Businesses must assess whether anticipated productivity improvements, operational benefits, or additional revenue justify these commitments.
The projected $1.4 trillion annualized spending level provides an indication of the potential financial requirements associated with AI development. Actual investment will depend on corporate budgets, available financing, and demand for computing capacity.
Frequently Asked Questions
How much could U.S. AI investment reach by 2027?
Goldman Sachs forecasts that annualized U.S. spending on artificial intelligence-related hardware and intellectual property could reach approximately $1.4 trillion by the end of 2027, compared with more than $900 billion projected for the end of 2026.
What is included in Goldman Sachs’ AI investment estimate?
The forecast covers spending on AI-related hardware and intellectual property. These categories include computing equipment, semiconductor-related technology, software, and intellectual assets used in AI development and deployment.
How much could AI investment contribute to U.S. GDP growth?
Goldman Sachs estimates that AI-related investment, including semiconductors, could add approximately 0.6 percentage points to U.S. GDP growth in 2027. Excluding semiconductor investment, the estimated contribution is 0.2 percentage points.
Which industries are connected to AI infrastructure spending?
Semiconductor manufacturing, data centers, computing infrastructure, and enterprise technology are among the sectors associated with AI-related expenditure. Companies supplying hardware, software, and supporting infrastructure may experience demand connected to these investments.
How could AI investment affect U.S. business spending?
Goldman Sachs projects overall U.S. business investment growth of approximately 8% on a quarterly basis in 2027, compared with a consensus estimate of 4.2%. AI-related expenditure is expected to be a major contributor, although actual spending will depend on financing conditions and corporate investment decisions.
Disclaimer: The financial projections and economic estimates discussed in this article are based on Goldman Sachs’ forecasts and are subject to change. Actual AI-related spending, business investment growth, and economic outcomes may differ depending on market conditions, corporate spending decisions, and other factors. The information is provided for general informational purposes only and should not be considered financial or investment advice.
