If there’s one thing artificial intelligence (AI) excels at, it’s defying every attempt to build a coherent, universally agreed-upon narrative around its multifaceted impact. The discourse surrounding AI oscillates wildly: Is it a job destroyer or a market masker? Are data centers unlocking unprecedented prosperity or hemorrhaging value faster than a new car driven off the lot? While the consequences of AI for the future remain a subject of intense debate, one of the more widely accepted views among economists, until recently, was that robust tech spending on AI initiatives was a crucial pillar, propping up an otherwise dismal global economy, particularly in the United States.

Throughout 2025, a broad consensus emerged among financial analysts and economic experts: the tech industry’s massive outlay on AI – encompassing everything from the construction of colossal data center infrastructure and soaring energy bills to exorbitant salaries for specialized talent and significant lobbying efforts – was responsible for a sizable chunk of the US’s Gross Domestic Product (GDP) growth. While estimates varied on the exact percentage, the underlying message was clear: investments in AI were critically important to the health and dynamism of the American economy. Major tech giants, often dubbed "hyperscalers," were pouring billions into AI research and development, acquiring startups, and building out the computational backbone necessary to power advanced AI models. Companies like Nvidia, whose market capitalization soared on the back of its AI chip dominance, became emblematic of this investment frenzy, further solidifying the narrative that AI was a potent economic stimulant. The sheer scale of capital expenditure in the sector led many to conclude that this unprecedented wave of spending was directly translating into domestic economic expansion.

However, a groundbreaking analysis from one of the world’s leading investment banks, Goldman Sachs, is now throwing its considerable weight behind a starkly different theory: that AI spending has had a negligible, if any, direct impact on the US economy whatsoever. This contrarian view challenges the very foundation of the previously held consensus, forcing a reevaluation of how we measure and attribute economic growth in the age of AI.

In a recent discussion on AI and its implications for the global economy, as reported by the Washington Post, Joseph Briggs, Goldman Sachs’s joint-lead of global economics investment research, articulated this astonishing claim. He argued pointedly that investment spending on AI had a "basically zero" impact on US GDP growth last year. Briggs reflected on the widespread belief, stating, "It was an intuitive story. That maybe prevented or limited the need to actually dig deeper into what was happening." This statement suggests that the allure of a simple, compelling narrative – AI equals growth – overshadowed the necessity for rigorous, detailed economic scrutiny.

This perspective appears to be the firm consensus among the analysts at Goldman Sachs, a financial institution renowned for its influential economic research. Goldman’s chief economist, Jan Hatzius, echoed Briggs’s sentiments, providing a more detailed explanation of the underlying economic mechanics. In a recent interview, Hatzius stated, "We don’t actually view AI investment as strongly growth-positive. I think there’s a lot of misreporting, actually, on the impact that AI investment had in US GDP growth in 2025, and it’s much smaller than is often perceived because most AI equipment is imported. That means there’s a positive entry in the investment line, but that’s offset by a negative entry in the net-exports line."

To fully grasp Hatzius’s argument, it’s crucial to understand the components of GDP. GDP is commonly calculated as the sum of Consumption (C), Investment (I), Government Spending (G), and Net Exports (X – M, where X is exports and M is imports). When US companies invest heavily in AI infrastructure – purchasing advanced chips, servers, and other hardware – this typically contributes to the "Investment" (I) component of GDP. However, Hatzius and his team contend that a substantial portion of this AI equipment, particularly the cutting-edge semiconductors and complex machinery required to produce them, is not manufactured domestically. Instead, it is imported from other countries.

This dynamic creates an accounting offset. While the domestic purchase of these imported goods increases the "Investment" component, the act of importing them simultaneously increases the "Imports" (M) component in the net exports calculation. If the value of the imported AI equipment is roughly equivalent to the investment made, then the positive impact on ‘I’ is largely canceled out by the negative impact on ‘(X-M)’, resulting in a near-zero net effect on US GDP.

In simpler terms, while nobody disputes that the US tech sector is pouring a tremendous "boatload of cash" into AI, the ultimate destination of that capital matters profoundly when measuring its direct impact on the domestic economy. Hatzius elaborated on this point, explaining, "A lot of AI investment that we see in the US adds to Taiwanese GDP, and it adds to [South] Korean GDP, but not really that much to US GDP." This is because key players in the semiconductor supply chain, such as Taiwan Semiconductor Manufacturing Company (TSMC) in Taiwan, which produces the most advanced AI chips, and companies like Samsung and SK Hynix in South Korea, critical for memory components, are the primary beneficiaries of this spending. The manufacturing, engineering, and associated economic activity generated by this demand largely occur in these foreign nations, rather than domestically within the United States.

This reinterpretation of AI’s economic influence comes at a time when Goldman Sachs is also taking concrete steps to reflect its analytical stance in its financial offerings. The bank recently launched SPXXAI, an inelegantly named S&P 500 index that specifically excludes stocks related to AI. This innovative financial product serves as a new tool for investors who are either looking to diversify their portfolios away from the perceived "AI bubble" or are simply seeking investment opportunities that are not heavily exposed to the sometimes volatile and highly concentrated AI sector. By creating such an index, Goldman Sachs is not merely offering an opinion; it is providing a tangible mechanism for investors to act on the very theory its economists are championing. This move underscores the conviction behind their analysis, signaling a belief that the market’s current valuation of AI-centric companies might be detached from the sector’s actual domestic economic stimulus.

The veracity of Goldman’s claim remains a subject of ongoing debate, partly because much of the data regarding AI’s precise economic impact is, as the original article states, "murky at best." AI is a rapidly evolving, complex field, making it difficult to categorize and measure using traditional economic metrics. Distinguishing between raw investment spending (capital expenditure) and the more elusive, often lagging, productivity gains driven by AI presents a significant challenge. Goldman’s argument primarily focuses on the former, suggesting that the initial investment wave isn’t translating into direct domestic GDP growth as widely assumed. However, this doesn’t necessarily preclude future productivity enhancements from eventually boosting the economy, although those effects would likely manifest with a time lag and are harder to quantify in the immediate term. The difficulty in isolating AI’s specific impact from broader technological advancements or other macroeconomic factors further complicates the picture.

One thing seems increasingly certain: the longer we spend in this "AI-no-man’s-land," characterized by rapid innovation, unprecedented investment, and uncertain outcomes, the less cohesive and singular any one narrative about its impact seems to become. Goldman Sachs’s analysis forces a more critical and nuanced examination of AI’s economic footprint, moving beyond the superficial headlines of massive spending to understand where the value truly accrues. It also highlights the globalized nature of modern technology supply chains and the challenge for any single nation to capture all the economic benefits of a transformative technology. For policymakers in the US, this perspective could spur further discussions on domestic manufacturing capabilities, incentivizing the production of critical AI components onshore to ensure that future investment spending translates more directly into domestic GDP growth. Ultimately, understanding AI’s true economic effects requires a sophisticated lens, one that Goldman Sachs is now actively providing, urging us all to dig deeper into what is truly happening beneath the surface of the AI revolution.