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Posted on 09/07/2026 8:20:09 AM PDT by SeekAndFind
The U.S. AI infrastructure buildout is reaching unprecedented levels, with the number of large-scale data centers and planned investment surging as technology companies expand capacity to meet growing AI demand.
There are now about 90 U.S. data centers with more than 100 megawatts (MW) of power in operation, the highest number on record, according to The Kobeissi Letter . If all currently planned projects are completed, that figure could more than triple to about 280 by 2030.
The expansion comes alongside an enormous spending cycle. Technology companies are expected to invest about $7T in data centers through 2030, underscoring the scale of the infrastructure required to support AI workloads.
The major hyperscalers are already committing heavily to that buildout. Amazon, Microsoft, Alphabet, and Meta have signed more than $1.5T in data center leases since the AI boom began, including commitments that have yet to take effect, Kobeissi said.
The buildout extends well beyond hyperscalers, creating a broad opportunity across the AI infrastructure supply chain. Semiconductor and networking companies are investing to support higher computing demand, while server, power, and data-center operators are expanding capacity.
Key publicly traded companies tied to the buildout include Nvidia (NVDA), AMD (AMD), Broadcom (AVGO), Marvell Technology (MRVL), Micron Technology (MU), Arista Networks (ANET), Credo Technology (CRDO), Dell Technologies (DELL), Hewlett Packard Enterprise (HPE), Super Micro Computer (SMCI), Vertiv (VRT), TSMC (TSM), Applied Materials (AMAT), Lam Research (LRCX), KLA (KLAC), Qualcomm (QCOM), GlobalFoundries (GFS), Oracle (ORCL), CoreWeave (CRWV), Nebius (NBIS), Equinix (EQIX), Digital Realty (DLR), Eaton (ETN), Quanta Services (PWR), GE Vernova (GEV), and Constellation Energy (CEG).
The scale of planned capacity and spending highlights how the AI expansion is becoming one of the largest capital spending cycles in history.
(Excerpt) Read more at seekingalpha.com ...
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Here’s what they’re coming after:
Majors:
✖️Global and Int. Studies and Statistical and Data Sciences
✖️Environmental Studies
✖️Environmental Geoscience
✖️Political science
✖️Communication Disorders
Buy a Stihl at a Stihl dealer.
You will never need to buy another one EVER.
I have had one for over thirty years.
FS 85 straight shaft bicycle grip blade trimmer.
It will take down a 2” sapling with one wack.
With two wacks it will take down a 3” tree.
More than automobiles. Elon Musk's companies are planning to put their new AI chips in many appliances, starting with kitchen appliances. If they can control driving cars or humanoid robots, the new chips can allow all appliances to be smart and interact with cloud data centers. Vacuum cleaners, air-conditioners, gardening tools... computer controls will be everywhere. Imagine front door locks with chips, deciding who to lock out of a home.
Sounds great. Until Trudeau finds out that you were one of the truckers involved in the covid protests.
You'll get locked out of your house.
I’m not ready for a smart toaster. Unless it can do raisin bread.
The future stuff happening now is scary. Front door locks with chips may turn against you, stopping your entry. And if you try to force your way in, they'll call government security, sending your picture directly. Then drones will come and put you down. Only thing to do is to invest in AI stocks and hope a nightmare does not happen.
Was talking to my wife last night about the ability to do stuff without modern things, including making bread. So she says, if you can't buy from stores how will you make bread. How will you make flour, or yeast, or other ingredients. How to find wheat, mill it, etc. No, I can't make raisin bread. I do have grapevines, but haven't made raisins. I need technology and welcome our AI overlords, because it is hard to create stuff.
No, they can’t scrub toilets but an Optimus Robot from Tesla can. And they’re about $25,000 each.
I know some hotel managers who are considering them for housing keeping roles because the pay is so low it’s hard to keep anyone.
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