Alphaville: "[There's a] mismatch between consensus forecasts for chip sales and US data centre completions...Morgan Stanley measured the gap earlier this week by estimating the shortfall in available power, concluding that >1/2 of the GPU servers sold between 2026 and 2028 might not have anywhere to be plugged in. Research from Jefferies reaches a similar conclusion using a very top-down method: space. Tracking US data centre construction sites by satellite imagery shows between 16 and 18 GW of gross capacity that can be energised this year...The rush to complete what’s already started makes it likely that for next year, data centre deployment by GW probably can’t go much above the low twenties. This represents a doubling versus the 11 GW deployed in 2025, but is a huge shortfall when compared with what’s implied by chip sales forecasts." This gets to two points I made in my recent report on "The AI Trade" (sageroadresearch.com/product…). First, hyperscalers appear likely to fail to build out the data center capacity required to improve their models enough to inspire adequate enterprise spending. To quote the report: "Delays have always been part of the data center construction reality—historically, only 72% of data center capacity has come online on time. However, things are clearly getting worse. Goldman expects only about 1/2 of the AI computing capacity scheduled to activate between now and 2028 via data center construction will actually come online by its target date. As asset valuation firm Barkr calculated in an August report, delays at that rate would translate to a compute supply/demand gap of 13.4 to 19.2 GW in 2027 and 27.3 to 36.8 GW in 2028." Second, market participants are underestimating how much chip stockpiling has been happening and what that could mean for chip demand expectations moving forward. To again quote the report: "There’s always been an element of suspension of disbelief in the triple-digit percentage stock gains made by everyone from Nvidia to Broadcom to TSMC, Micron, AMD, and GE Vernova—as if the AI revolution had rendered cyclicality a challenge of the past. Now, AI spending’s rate of change is poised to slow. Politics is likely to increase fear that the pace of the AI data center buildout falls short of expectations. And the threat grows that one or more hyperscalers decides to pull back from the AI arms race. If AI CAPEX slows, it’s likely to hit picks-and-shovels earnings greater than most will anticipate. From hardware shortages has come stockpiling. As one VC noted recently: “In some data centers, I’m hearing the usage of graphics processing units is only around 35% or 40% because some companies are hoarding colossal amounts in case they come to a point at which they don’t have enough chips to provide the computing capacity.” FT link: ft.com/content/4c3f75a2-c2f9…