Goldman warns AI economic impact not seen in GDP, leaving $115b blind spot

Goldman Sachs finds a $115B blind spot

Goldman Sachs estimates roughly $115 billion of AI-driven economic activity since 2022 is not reflected in official U.S. GDP figures, part of a wider estimate that AI has added about $160 billion in activity while only around $45 billion shows up in government statistics.

This “blind spot” is a significant gap between companies’ reports and government measures.

BEA’s methodology misses AI growth

This uncounted growth results from the Bureau of Economic Analysis (BEA) method for calculating growth. The BEA considers semiconductors as “intermediate inputs.” This means they only count towards a product’s final value when it is sold, for example, a consumer laptop.

The issue is that AI chips are often used to train AI models. This creates an intangible asset, not a final product. Since this value isn’t fully measured, it creates a large “blind spot” in the official numbers.

Import policies distort AI investment

Tariff threats in 2025 prompted many firms to front-load purchases of servers and networking gear, creating a temporary spike in imports and capital spending that complicates short-term readings of investment trends and GDP.

Analysts say the timing of those purchases, not just their size, can make it harder to disentangle genuine demand growth from timing effects.

AI propels S&P 500 to highs

A separate report from Goldman Sachs showed that AI has helped the S&P 500 stock index reach all-time highs. Most companies still struggle to show how AI affects their earnings.

The top eight publicly traded companies in the S&P 500 are heavily invested in AI. These companies, including tech giants, make up more than 36% of the S&P 500 and ramp up their AI spending to release new products and services.

Stock market at risk if AI spending slows

Goldman Sachs has warned that the stock market could drop significantly if AI spending slows down. Analyst Ryan Hammond stated that a revision of long-term growth estimates back to early 2023 levels would mean a 15% to 20% downside to the current value of the S&P 500.

This warning highlights that the market’s high value is tied to the expectation of continued, substantial AI investment. The danger is that companies, especially “hyperscalers,” might reduce their AI spending.

Meta’s massive AI investment

While some analysts expect a slowdown in AI spending, tech giant Meta Platforms has pledged a massive investment.

Meta has said it expects to invest at least $600 billion in U.S. infrastructure through 2028, a broad ‘envelope’ that the company’s CFO says covers data centers, people and other investments tied to scaling AI capacity, rather than a precise, line-item three-year spending plan.

This shows a long-term commitment to AI that contrasts with the temporary rush seen earlier in the year.

Nvidia dominates the S&P 500 index

Nvidia is one of the largest single components of the S&P 500. Slickcharts reports Nvidia’s weight at roughly 8% of the index today, underscoring how concentrated the market’s gains have been among a few AI-exposed names.

That weight helps explain how Nvidia’s moves can strongly influence broader market performance. Nvidia’s graphics processing units (GPUs) are essential for developing and training AI models.

The high demand for its products has made it one of the top performers in the market, helping to fuel the S&P 500’s rise.

Oracle’s contribution to AI

Oracle is also a significant player in the AI space. It is one of the companies, along with Palantir and Cisco, that is heavily invested in the technology.

As of the latest S&P 500 component weights, Oracle (1.19%) + Palantir (0.80%) + Cisco (0.47%) add up to roughly 2.5% of the index.

Oracle’s continued increase in AI investments, especially with its cloud infrastructure, could help the stock market continue to grow. The company is actively building its presence to support the high computing needs of AI.

Oracle’s database dominance (2020)

Oracle’s success in AI is built on its history as a leader in enterprise software and databases. By 2020, Oracle held a dominant share of the database market. Its Oracle Database software is widely used by Fortune 500 companies to manage their most important data.

Oracle’s shift to AI and cloud computing was a logical step for the company. Its decades of experience managing vast amounts of data made it well-suited to handle the needs of AI, which requires massive data processing.

Palantir’s early government contracts (2010s)

Palantir’s history is rooted in its work with U.S. government agencies. In the 2010s, the company became known for providing data analysis tools for intelligence and defense. This work gave it a strong foundation in handling complex data sets.

For example, Palantir provided software to the U.S. Army to help with military intelligence. This early work laid the groundwork for the company’s current AI and analytics platforms used by governments and businesses today.

Cisco’s internet infrastructure (2000s)

Cisco’s place in AI infrastructure continues its past role in providing the networking hardware that enabled the internet to grow. In the late 1990s and 2000s, Cisco became a dominant provider of routers and switches, essential to the internet.

While Cisco did not create the internet, its products were critical for connecting the growing number of networks and companies that used the internet. This made Cisco a central player in the rapid growth of the commercial internet.

Nvidia’s graphics card evolution (1990s)

Nvidia’s dominance in AI started with its work on graphics cards for video games. 1999, the company launched the GeForce 256, widely recognized as the first GPU.

This chip was designed to handle the massive number of parallel calculations needed for graphics, a skill that was perfect for the complex math required to train AI models. The success of its GPUs in gaming laid the technical foundation for its future in AI.

Meta’s social media foundations (2004)

Meta’s massive AI investment is a natural progression from its roots in social media. The company was founded as Facebook in 2004, with a business model that analyzed user data to deliver targeted advertising.

This meant the company was built on data science and machine learning. Meta’s early work in understanding user behavior gave it the necessary expertise and data to become a leader in AI research and development.

Goldman Sachs’s role in finance (1869)

Goldman Sachs, the company that published the report, has been a leading financial institution since 1869. The company has a long history of analyzing economic trends and advising governments and businesses.

It is known for detailed research on markets, economies, and specific industries. The report on AI is an example of Goldman Sachs continuing its long-standing role of analyzing new technologies and their potential economic impact.

Donald Trump’s previous economic policies (2018)

Donald Trump’s previous economic policies, such as the 2018 tariffs on steel and aluminum, showed his willingness to use import taxes. This created an expectation among companies that new tariffs could be implemented.

The 2025 rush to import AI equipment can be linked to this history. Companies likely frontloaded their purchases to avoid new tariffs, but this behavior also depends on other economic and geopolitical factors, not just past policies.

Curious how this changes politics and tech? Read more in the world’s first AI minister is here.

The future of AI and the markets

The Goldman Sachs report shows that the current U.S. economic system is struggling to measure the impact of new technologies. The uncounted $115 billion shows a gap between real economic activity and official data.

This gap challenges leaders trying to understand the economy’s proper health. The stock market’s reliance on a few AI companies and the potential for a slowdown in spending show that the future is both promising and uncertain.

Wondering how your conversations are being used? Find out in your Claude chats help train AI – here’s how to stop.

What’s your take on this gap: game-changer or oversight? Share your thoughts in the comments.

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This slideshow was made with AI assistance and human editing.