Goldman Sachs Predicts $1 Trillion in Global AI Investment

Global spending on artificial intelligence could reach $1 trillion in 2026, according to a new Goldman Sachs forecast. The figure puts AI among the largest areas of corporate technology spending and points to a sharp increase in money flowing into computing infrastructure, data centers, software, and AI services.

Why Goldman Sachs expects AI spending to surge

The forecast reflects the enormous cost of building and operating AI infrastructure. Training advanced models requires large amounts of computing power, while serving those models to millions of users creates another continuing demand for processors, electricity, networking equipment, and data-center capacity.

Technology companies are therefore directing large budgets toward AI hardware and infrastructure. Cloud providers need more computing capacity, chipmakers are supplying processors designed for AI workloads, and businesses are spending on software that can put AI models into everyday operations. The spending does not come from a single industry or a single type of company.

What the $1 trillion figure includes

AI investment covers much more than buying access to a chatbot. A large share of the spending is tied to physical infrastructure, including servers, accelerators, networking hardware, power systems, and data centers. Companies also spend on model development, cloud services, enterprise software, research, and staff.

This distinction matters when interpreting the forecast. A $1 trillion investment figure does not mean businesses will spend that amount directly on consumer AI applications. Much of the money can move through the infrastructure chain before an AI service reaches an end user.

AI spending could affect global economic growth

Goldman Sachs expects the investment surge to have a measurable effect on global GDP by 2028. The basic economic mechanism is straightforward: companies buying equipment and services create demand for suppliers, while new AI systems could allow businesses to produce more output with the same workforce or complete certain tasks in less time.

The size of that economic effect will depend on how quickly companies turn AI spending into productive use. Building another data center increases construction and equipment demand immediately, but the longer-term economic return depends on whether businesses can use the resulting computing capacity to improve products, automate work, or develop new services.

The cost of AI infrastructure remains a major factor

AI investment also brings large operating costs. Data centers require electricity and cooling, while high-performance processors can be expensive to purchase and replace. Companies must balance those expenses against revenue from AI products and the productivity gains expected from internal deployments.

That balance will shape whether the spending pace can continue. If businesses see clear financial returns from AI systems, investment can remain high. If adoption produces weaker returns than expected, companies may become more selective about new infrastructure projects and software budgets.

What to watch through 2026

The most useful measure will be actual capital spending rather than announcements alone. Data-center construction, processor orders, cloud capacity, corporate AI budgets, and revenue from AI products will provide clearer evidence of whether the $1 trillion projection is being approached.

Goldman Sachs' forecast also places attention on the period beyond 2026. If the investment wave produces measurable productivity gains, the economic effect could extend well beyond the companies selling AI hardware and software. The report's projection of a boost to global GDP by 2028 makes the next two years especially important for judging whether the spending is producing corresponding economic output.