$700 Billion Pouring into AI, Americans Are First to Taste the Bitter Fruit of Inflation
- Core Viewpoint: The current optimistic expectations for AI and massive capital expenditures themselves have become drivers of inflation. By stimulating aggregate demand and exacerbating resource bottlenecks such as electricity and chips, they may subject the U.S. economy to short-term inflationary pressures. Whether AI can deliver on productivity gains to offset inflation remains an open question.
- Key Elements:
- Capital expenditures by major tech companies have surged. Amazon, Microsoft, Google, and Meta collectively reached $251 billion in 2024. It is projected that by 2026, the total for five companies (including Oracle) will approach $700 billion, representing an unprecedented growth rate.
- Data center power demand is enormous. For example, OpenAI's Stargate project is planned to have a power capacity of 10 gigawatts, equivalent to the total electricity consumption of 16 Vermont states. U.S. data center power consumption is expected to triple by 2030.
- Deutsche Bank experiments show that multiple AI models assess the probability of AI pushing inflation higher in the next year (20%-40%) as far greater than the probability of significantly lowering inflation (5%), with primary concerns centered on demand-pull price pressures.
- Research from the St. Louis Fed points out that the AI investment boom, as a type of "news shock," stimulates current consumption and investment by raising household and business expectations, thereby generating an inflationary surge in aggregate demand in the initial phase.
- The key contradiction lies in the fact that despite massive investment, AI has yet to leave a clear mark on productivity data (TFP growth remains below historical averages). Meanwhile, U.S. core inflation has not yet returned to the 2% target, thereby constraining the Federal Reserve's path to interest rate cuts.
On April 1st, St. Louis Fed economists Miguel Faria-e-Castro and Serdar Ozkan published a blog post with a restrained title but a sharp conclusion: AI optimism itself is a driver of inflation. Not because electricity prices are rising, not because of chip shortages, but because everyone believes AI will make the future better—this belief makes them spend more money now.
On the same day, Fortune disclosed an experiment by Deutsche Bank: they asked three AI models to assess "the impact of AI on inflation." The conclusion was that even AI itself believes it is pushing up prices.
On social media, posts about soaring prices in the US are abundant.
These two pieces together point to an uncomfortable cycle: the more AI investment, the higher inflation, the further away rate cuts are, the higher financing costs—yet investment continues to accelerate.
The Unstoppable Arms Race
First, look at the money. According to company financial reports, the combined capital expenditures of Amazon, Microsoft, Google, and Meta in 2023 were approximately $152 billion. By 2024, this number jumped to $251 billion, a 65% increase. For the full year 2025, it settled at $416 billion, another 66% increase.
Guidance for 2026 is even more aggressive. According to Wolf Street's compilation, Amazon guides for $200 billion, Google guides for $175-185 billion, Microsoft guides for $145-150 billion, and Meta guides for $135 billion. The four combined total about $663 billion. Including Oracle's $42 billion, the total for five companies approaches $700 billion.
In four years, the capital expenditures of these four companies have quadrupled. This growth rate is unprecedented in US corporate history. According to a Fortune report, this scale already exceeds Sweden's annual GDP.
One Data Center Consumes as Much Power as an Entire State
Most of this money flows into data centers. And the biggest bottleneck for data centers is not land, but electricity. According to EIA data, Vermont's annual electricity consumption is about 5,364 gigawatt-hours, translating to an average load of 0.61 gigawatts. Rhode Island is slightly higher, at about 0.83 gigawatts.
Now look at what data centers are doing. According to company announcements, the total planned power capacity for the Stargate project, a collaboration between OpenAI, Oracle, and SoftBank, is 10 gigawatts, equivalent to the total electricity consumption of 16 Vermonts. Meta's Hyperion campus in Louisiana is planned for 5 gigawatts, with a $27 billion investment. Musk's xAI's Colossus facility in Memphis, Tennessee, has expanded to 2 gigawatts; according to an Introl report, it deployed 555,000 Nvidia GPUs, costing about $18 billion. Amazon and Anthropic's jointly built Project Rainier in Indiana is planned for 2.2 gigawatts.
According to S&P Global data, total US data center power consumption in 2024 was 183 terawatt-hours, accounting for over 4% of national electricity usage. By 2030, this number is expected to triple.
This electricity demand is not a distant future story in planning; it is already straining existing grids. According to a CBRE report, the vacancy rate for North American data centers dropped from 3.3% in the first half of 2023 to a record low of 1.6% in the first half of 2025. According to Cushman & Wakefield data, the vacancy rate slightly recovered to 3.5% in the second half of 2025, but that was only due to a concentrated delivery of massive new capacity—absolute levels remain historically low, and meaningful supply relief is unlikely before 2030.
Even AI Itself Says It's Pushing Up Inflation
While these investments drive demand, push up electricity prices, and exacerbate chip shortages, there is an even more hidden inflation channel.
According to a Fortune report on April 1st, a team led by Deutsche Bank's chief US economist, Matthew Luzzetti, conducted an experiment: they asked Deutsche Bank's proprietary model dbLumina, Anthropic's Claude, and OpenAI's ChatGPT-5.2 to respectively assess "the probability that AI will push up inflation over the next year."
Results: dbLumina gave 40%, Claude gave 25%, and ChatGPT-5.2 gave 20%. All three models were consistent in their assessment of the probability of "AI significantly lowering inflation": only 5%.
The inflation drivers cited by the three models were highly consistent: data centers are undergoing massive expansion, semiconductor demand is soaring, and the power consumption of AI workloads is growing rapidly—all of these are demand-pull price pressures.
This is the opposite of the consensus among some Wall Street investors. The Deutsche Bank team wrote in their research report: "Will AI be a major deflationary force? Even AI itself doesn't think so."
On a five-year horizon, the models did shift towards more deflationary possibilities. But the probability of "AI causing large-scale deflation" remains relegated to the tail risk zone.
Optimism Itself Is Inflation
The St. Louis Fed paper provides a theoretical framework to explain all of this.
Faria-e-Castro and Ozkan used a standard macroeconomic model, defining the AI investment boom as a "news shock." According to the Fed blog post, the model's logic is: when households see AI described as a revolutionary technology, they expect future income to rise and increase consumption in advance. Firms anticipate productivity gains and ramp up investment. The combination of the two causes demand to quickly outstrip supply. The paper states: "These forces together generate an inflationary surge in aggregate demand—a key feature of the initial phase of a news shock."
The model outlines two paths. If AI does deliver a productivity leap, short-term inflation will be absorbed by long-term output growth, and the economy enters a virtuous cycle. But if productivity gains fail to materialize—the paper uses the term "persistent low growth and stubbornly high inflation," i.e., stagflation.
According to data cited in the Fed blog post, the annualized growth rate of US Total Factor Productivity (TFP) since the release of ChatGPT has been 1.11%, lower than the historical average of 1.23%. So far, AI has left no trace in the productivity data.
Meanwhile, according to BLS data, the US CPI in February 2026 rose 2.4% year-on-year, with core CPI at 2.5%, neither having returned to the Fed's 2% target. The Fed's March dot plot shows a median year-end rate forecast of 3.4%, pointing to only one rate cut this year.
$700 billion is pouring into AI infrastructure. Whether this money is a cause of inflation or a prelude to a productivity revolution depends on a question no one can yet answer: will the models running in these data centers actually make the economy more efficient?
