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Our three favorite AI utility names: • $NBIS builds across the software + developer layer • $IREN builds down into power + compute • $CIFR rents out the physical layer through long-term leases

Shay Boloor

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158,442 views • 7 months ago •via X (Twitter)

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Goldman Sachs expects data center power demand to triple by 2030. Last week I broke down two of my favorite AI utility names $NBIS & $IREN with Charles on air. I didn’t have time to mention $CIFR but it’s in the same tier.

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$NVDA CEO Jensen Huang revealed the Five Layer AI Model These 5 layers include: 1) Chips - $AMD $AVGO $NVDA 2) Memory - $MU $SNDK $DRAM 3) Photonics - $AAOI $AXTI $AEHR 4) Compute - $CIFR $IREN $NBIS 5) Energy - $BE $FLNC $TE

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$NBIS reminds the market that energy and capacity are still heavily constrained and hyperscalers have insatiable compute need. Good for $CIFR $CRWV and $IREN as well.

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How to invest across every layer of $NVDA CEO Jensen Huang’s five-layer cake: • Layer 1 Energy (The Fuel): $CEG, $VST • Layer 2 Chips (The Engine): $TSM, $MU • Layer 3 Infrastructure (The Factory): $NBIS, $VRT • Layer 4 Models (The Intelligence): $GOOGL, $META • Layer 5 Applications (Where Value Is Proven): $PLTR, $NET

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Nvidia CEO Jensen Huang just revealed the “five-layer” model of what AI is dependent upon… These 5 layers include: 1. Energy ~ $NBIS, $IREN, $CIFR, $OKLO 2. Chips ~ $NVDA, $AMD, $TSM, $AVGO 3. Cloud Infrastructure ~ $AMZN, $MSFT, $GOOGL, $CRWV 4. Models ~ $META, $ORCL 5. Applications ~ $PLTR, $TSLA All these names will see generational upside in 2026 as AI continues to expand rapidly. Save this for later…

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Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

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$IREN co-founder Daniel Roberts said if someone wanted to build a 1GW AI factory TODAY, they likely would not get first compute online until 2030, reinforcing how severe the power bottleneck has become across AI infrastructure. That is exactly why energy and power infrastructure in $BE and $FPS continue becoming increasingly important to support the expanding compute footprint tied to the builders $NBIS, $WULF, and $APLD.

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