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Silicon Valley moves fast and breaks things, but the U.S. electrical grid moves slowly and requires permits. The explosive growth of AI is colliding with the glacial pace of building out new power infrastructure, prompting a new warning from Goldman Sachs that energy bottlenecks are set to stall the AI race. Why it matters : Amid a wave of anxiety from Wall Street about the debt-fueled AI buildout, Goldman points to power as the biggest risk to trillions in spending in a new report shared exclusively with Axios. Driving the news: Goldman expects global data center power demand to surge 170% between 2025 and 2030, with more than 60% of that growth coming from the U.S., according to the report . "Power is probably the forever bottleneck," Jason Tofsky, Goldman's global head of digital infrastructure investment banking, noted in the report. The bank estimates roughly $7.6 trillion in capital spending between 2026 and 2031 across computing, data centers and power infrastructure. Zoom in: The physical infrastructure needed to support AI is moving far more slowly than the technology itself. The U.S. has roughly 2,600 gigawatts of proposed power generation and storage projects awaiting grid connections, more than twice the grid's existing installed capacity, though many projects may never materialize. The median time from an application for a grid connection to commercial operation is roughly five years. Between the lines: The so-called AI race, which has long been defined by advances in frontier AI models, is really about who can get their hands on enough power, Goldman argues. That could mean a longer, less predictable buildout, delayed financial returns and greater risks for companies investing heavily before securing power. Follow the money: The massive expansion is being financed with debt, raising the stakes if construction timelines slip. Goldman projects $420 billion in global hyperscaler debt issuance in 2027, up more than 60% from its 2026 estimate. Debt is expected to finance roughly 35% of hyperscaler capital expenditures next year. And hyperscalers account for just 40% of AI ecosystem credit fundraising, underscoring how widely the borrowing extends. The intrigue: Since January, 86 projects have been canceled or significantly delayed, including 76 directly linked to local opposition, according to Goldman. Put together, this paints a picture of extended delays. Yes, but : Slower deployment could also help prevent over-investment by giving actual demand time to catch up with spending. Meanwhile: The boom is transforming utilities from traditionally slow-growing businesses into major beneficiaries of AI investment. Utility capital plans are expected to reach $1.3 trillion between 2026 and 2030. Tech giants are considering arrangements to directly fund the power generation, transmission lines and electrical infrastructure their data centers require. The bottom line: If you build AI systems, users will come, but they can only stay if there's enough power to go around.