10 WAYS TO BUILD AN AI POWER PORTFOLIO
1. $OKLO effectively building the “local nuclear plant” the AI economy will require by placing reactors directly next to data center campuses for 24/7 onsite generation.
2. $BE fuel-cell onsite power play helping data centers bypass the grid with dedicated energy for AI clusters with product backlog up 250% YoY to $6B.
3. $CEG nuclear baseload backbone of the AI era with a 20-year $MSFT PPA tied to the Three Mile Island restart to supply the 24/7 carbon-free power.
4. $VST hybrid power engine of AI combining nuclear, gas & storage with a 20-year $META agreement covering 2,600+ MW across three nuclear plants.
5. $GEV industrial supplier rebuilding the U.S. grid providing the turbines, transformers & hardware every AI-driven upgrade cycle depends on with $163B in backlog.
6. $VRT infrastructure gatekeeper for AI compute controlling the cooling & power systems that $NVDA class clusters cannot run without with Q1 backlog up 80% YoY to ~$12.5B.
7. $EOSE long-duration storage solution for a grid under strain helping utilities smooth volatility as AI demand overtakes supply.
8. $NEE clean-energy arm of the AI buildout with largest renewable development pipeline in the country positioned directly into data center load growth.
9. $LEU only U.S. source of HALEU fuel making it essential for powering the modular reactors needed around future AI campuses backed by ~3B DOE contract.
10. $UUUU secures the domestic uranium supply chain by turning nuclear fuel into a national-security asset for the AI age.
$NVDA has committed more than $40B to equity investments this year alone:
• OpenAI $30B
• $GLW ~$3.2B
• $IREN ~$2.1B
• $MRVL ~$2B
• $LITE ~$2B
• $COHR ~$2B
• $CRWV ~$2B
• $NBIS ~$2B
Nvidia is positioned across every layer of the AI buildout from silicon & networking to optics, power, neoclouds & the model companies running on top.
The whole stack runs through Jensen.
THE COMPUTE CAPACITY BOTTLENECK
$GOOGL just admitted Google Cloud is leaving revenue on the table because it cannot build capacity fast enough with shifts the bottleneck to companies with the power, real estate & operational scale to deploy AI compute:
1. $NBIS building the AI-native cloud layer through vertically integrated GPU clusters & software optimized for training + inference. $NVDA just wrote wrote a $2B check & Nebius now has a $46B contracted backlog, anchored by ~$19B $MSFT deal & ~$27B Meta partnership through 2032.
2. $IREN building the renewable-powered AI compute layer by turning low-cost power into GPU cloud capacity. The pivot to AI cloud is now backed by a ~$10B $MSFT contract, 2.9 GW of grid-connected power expanding to 4.5 GW+ & targeted 140K GPU buildout that could drive $3.4B of ARR by year-end 2026.
3. $DOCN building the agentic inference cloud layer for developers & long-tail AI workloads. AI customer ARR is up 150% YoY to $120M, over 70% comes from inference services & $1M+ customer ARR is up 123% to $133M.
4. $CRWV building the dedicated AI cloud platform for frontier model developers. The company has ~$67B of contracted revenue backlog (nearly $88B including Anthropic) with major commitments from OpenAI & Meta.
5. $CIFR building the Google-backed AI data center layer through contracted power & hyperscale leases. Barber Lake has 300 MW fully contracted with Fluidstack (Google backstops $1.4B with a ~5% equity stake) and AWS signed a separate $5.5B 15-year deal for another 300 MW of capacity.
6. $WULF building the power-backed AI compute layer through long-term data center leases. Lake Mariner has 360 MW tied to Fluidstack backed by a $3.2B Google guarantee & new Abernathy JV adds 168 MW over 25 years representing $9.5B in contracted revenue with $1.3B of Google lease support.
7. $APLD building the purpose-built AI data center layer through its Polaris Forge 1 campus in North Dakota. The full 400 MW critical IT load is contracted to CoreWeave under ~15-year leases worth ~$11B in expected revenue with first 100 MW delivered in Q4 2025 & another 300 MW targeted through 2027.