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At RAISE Summit in Paris, one theme stood out: every layer of the AI stack is up for grabs. Startups are challenging incumbents in chips, cloud infrastructure, data centers, and foundation models alike.

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Every AI response you've ever gotten started with an electron. ⚡ In a recent interview with Sequoia Capital, Jensen Huang described AI infrastructure as a five-layer cake, and at the very bottom, before chips, before data centers, before models, is energy. That's the binding constraint on the Intelligence Age: not compute, not software — watts. This is why NVIDIA calls AI factories the dynamos of our era. They take in electrons and send out tokens of intelligence. We're a few hundred billion dollars into what Jensen calls the largest infrastructure buildout in human history. Trillions more to go. The question isn't whether AI will scale. It's whether the energy grid can keep up. ⬆️

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$IREN at RAISE Summit Kent Draper: 3 layers of AI compute 1. Physical Infrastructure 2. Compute 3. Software & Services Layers 1 and 2 are the key foundation, without those, your ceiling is capped. I have been pounding the table on the superior model of a bottom-up approach. Kent Draper confirms that IREN sees it the same. Having a strong foundation of land, power, grid connections, substations, owned GPUs, network core buildings, and storage, will unlock the amount of GPU hours that can be monetized up the stack. It's very simple: with an abundance of physical infrastructure and chips — software is the layer that can be added when needed. The foundation of the thesis rests on physical assets and power being scarce, and those are the limiting factors in your ability to grow faster than peers.

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$80-100B per gigawatt. That is the current cost of a data center. Jordi Visser's (Jordi Visser) AI trade rotation thesis, via Anthony Pompliano 🌪: hardware and infrastructure are priced. Foundation models face commoditization. The next decade's returns are in applications with proprietary data - companies that don't rent intelligence because they built their own. Chips, energy, infrastructure: monetized. Models: under margin pressure. Applications with locked data moats: still in early innings and still priced as their legacy category. Eli Lilly fits the third bucket. So do other sector incumbents with 50+ years of proprietary data. Most are still priced as what they were, not what they are becoming. The full rotation framework: Source: Anthony Pompliano Podcast -
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$80-100B per gigawatt. That is the current cost of a data center. Jordi Visser's (Jordi Visser) AI trade rotation thesis, via Anthony Pompliano 🌪: hardware and infrastructure are priced. Foundation models face commoditization. The next decade's returns are in applications with proprietary data - companies that don't rent intelligence because they built their own. Chips, energy, infrastructure: monetized. Models: under margin pressure. Applications with locked data moats: still in early innings and still priced as their legacy category. Eli Lilly fits the third bucket. So do other sector incumbents with 50+ years of proprietary data. Most are still priced as what they were, not what they are becoming. The full rotation framework: Source: Anthony Pompliano Podcast -

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