The United States economy presents a facade of strength, with headline data suggesting continued expansion despite headwinds such as gasoline prices nearing $5, crude oil trading above $100, historically low consumer sentiment, and elevated interest rates for mortgages and other loans. However, a deeper examination of the economy's supporting structures is crucial to understanding the true impact of Artificial Intelligence (AI). This analysis, the first part of a two-part series, explores whether the current AI investment boom is genuinely strengthening the nation's economic footing or potentially weakening its labor force, the fundamental pillar of the economy. The optimistic case posits an AI-induced, productivity-led economic boom with benefits rapidly disseminating throughout society.
The scale of capital investment flowing into AI infrastructure development is substantial, significantly contributing to Gross Domestic Product (GDP). Capital expenditures (Capex) from just four major technology companies—Amazon, Google, Microsoft, and Meta—now exceed $700 billion annually, a sevenfold increase over the past five years. Projections for 2026 indicate that these four companies alone could account for one-third of GDP growth. This AI buildout extends far beyond these tech giants, creating demand across a wide supply chain. Construction firms are developing data center campuses, utility companies are increasing power generation capacity, domestic semiconductor manufacturers are scaling up production, and suppliers of fiber optics and networking equipment have multi-year order backlogs. The electrical grid, in particular, is experiencing its first sustained demand growth in two decades, primarily driven by data center power requirements, which are anticipated to more than double by 2030.
Historically, the current AI infrastructure spending has precedents. The railroad expansion in the mid-1800s involved more extreme infrastructure investment, with railway Capex estimated to have consumed between 10% and 20% of GDP at its peak. A more relevant comparison is the telecom buildout of the late 1990s, when Capex reached approximately 1.0-1.2% of U.S. GDP. Today's AI infrastructure spending by the aforementioned four companies has surpassed that telecom figure. A key distinction, however, is the funding mechanism. Unlike the debt-fueled telecom boom, current AI spending has been financed predominantly by the cash and cash flows of highly profitable corporations. While there is a shift from cash and free cash flow towards debt financing, the companies in question maintain debt-to-equity ratios significantly below the S&P 500 average and considerably lower than those observed during the telecom buildout. Furthermore, earnings from their other profitable business lines are expected to continue providing substantial capital for ongoing investment.
While AI spending is a significant economic driver, some analysts suggest it may be masking underlying weaknesses in consumer spending, the primary engine of economic growth. Consumer spending has consistently accounted for approximately 67% of GDP since 2001, with no discernible change in its contribution in recent years despite the advent of AI. However, the sustainability of this consumption level is a critical factor for future growth. Current signs indicate a potential deterioration in the means by which consumers are funding their spending. The personal savings rate has fallen to near its lowest level since 1960, suggesting that a larger portion of personal consumption is being financed by drawing down savings rather than by current earnings. This behavior is not uncommon during periods of strong employment, as consumers tend to spend more when confident about their job and wage prospects. Although a low savings rate serves as a yellow flag, it has previously coexisted with healthy economic expansions.
A more crucial indicator for future consumption is wages, which brings us to the labor market. While some pessimists predict that AI will lead to widespread job displacement, the data thus far does not fully support this. According to Challenger, Gray & Christmas, in 2025, approximately 55,000 out of 1.17 million layoffs were directly attributed to AI. Other estimates place the number of AI-related job losses higher, between 200,000 and 300,000 positions in 2025. Even with these higher figures, this represents only about 0.15% to 0.20% of total nonfarm employment. The future outlook, however, becomes less clear. Goldman Sachs projects that 300 million jobs globally are at risk due to AI. This perspective, however, only tells part of the story. The World Economic Forum (WEF) estimates that AI will simultaneously create 170 million jobs globally.
There is little doubt that AI will exert significant influence on the economy, the labor market, and individual lives. Historical precedents demonstrate this transformative power; approximately two-thirds of U.S. jobs from the 1940s no longer exist, replaced by roles enabled by new innovations. While the future remains uncertain, the historical relationship between job growth, wages, and productivity offers encouragement. PwC reports that wages are rising twice as fast in industries most exposed to AI compared to those least exposed.
Economic growth and wage growth are intrinsically linked to productivity, which measures an economy's ability to leverage its primary inputs: labor and capital. Without productivity gains, long-term economic growth becomes unsustainable, relying solely on limited inputs. Therefore, understanding the extent of AI-driven productivity and its distribution is paramount. Quantifying the exact productivity gains from AI at this early stage is challenging. However, PwC estimates that productivity growth has nearly quadrupled in AI-exposed industries since 2022. The question of whether AI is the direct cause of this surge is difficult to prove definitively. Nevertheless, revenue growth in AI-exposed industries accelerated sharply in 2022, the same year ChatGPT 3.5 was launched, bringing AI's capabilities to widespread attention. As companies have increasingly adopted this technology, the value generated in industries best positioned to utilize AI has surged. In just two years, industries that were once productivity laggards have become leaders, suggesting that AI investments are yielding significant returns. AI's promise appears to be materializing, and the adoption of this technology is still in its nascent stages.
Regarding the distribution of productivity gains, some argue that these benefits are disproportionately flowing to high-income knowledge workers. This pattern has been observed with every major technological wave in its early phases. Factory automation initially benefited capital owners, personal computers initially aided white-collar workers, and the internet initially favored the educated and connected. However, historical trends indicate that over time, prices decrease, adoption rates increase, and the benefits gradually spread across the entire workforce. The consistent verdict of history is that benefits initially concentrate but ultimately disseminate widely throughout the economy. This phenomenon is illustrated by the fact that the poorest states in the U.S., such as Mississippi, West Virginia, and Arkansas, exhibit similar or higher GDP per capita compared to other large nations, reflecting the broad economic impact of innovation in the U.S.
