$CEG 0.66 Rising Signal

Notable posts

• @StockSavvyShay neutral 1587 eng

  THE NEW AI INFRASTRUCTURE STACK Piper Sandler mapped the new AI infrastructure stack across 8 layers from the power grid all the way to the agent as AI scaling creates bottlenecks across power, cooling, networking, storage, cloud & inference: Layer 1 | Power, Grid & Real Estate • $NEE building renewable power backbone hyperscalers need to scale AI • $CEG monetizing 24/7 nuclear power AI data centers increasingly require • $TLN building behind the meter nuclear power model for AI campuses • $IREN turning scarce power & interconnects into hyperscale AI capacity • $DLR owning physical real estate underneath global AI infrastructure • $EQIX owning interconnection layer where AI networks & clouds meet Layer 2 | Physical Datacenter, Shell & Cooling • $VRT building 800V DC powertrain needed for next jump in AI rack density • $SMCI assembling liquid cooled rack systems that turn GPUs into usable compute Layer 3 | Silicon & Hardware • $NVDA building compute platform underneath AI economy • $AMD building second full stack accelerator platform at hyperscale • $AVGO designing custom AI silicon behind Google TPUs & Meta MTIA as hyperscalers move deeper into proprietary compute • $GOOGL turning TPUs from an internal advantage into a hardware business sold directly into customer data centers • $INTC turning 14A into a real external foundry business with $TSLA as an anchor customer and its largest reported foundry deal yet • $ARM moving into full server CPUs & capturing the full value of the chip instead of dollars per core Layer 4 | Scale Up & Scale Out Networking • $ANET building Ethernet backbone connecting hyperscale AI clusters • $CSCO extending enterprise networking into AI data center fabric • $CLS manufacturing physical networking hardware connecting AI clusters Layer 5 | High Performance Storage • $PSTG using DirectFlash to reclaim power & rack space for AI infrastructure • $DELL bundling servers & storage into an integrated enterprise AI stack • $NTAP connecting enterprise data estates to cloud AI workloads • $HPE bringing enterprise storage into an infrastructure as a service model Layer 6 | Orchestration & Workloads • $IBM turning Red Hat OpenShift into enterprise control plane for private & hybrid AI deployments • $NTNX bringing AI infrastructure stack into private & on prem environments Layer 7 | GPUaaS & Cloud • $CRWV proving GPU residual value with fully priced A100 capacity contracted through 2029 • $NBIS building an AI cloud around 5GW of contracted power with customers prepaying 50% of capex • $DOCN bringing AI compute & inference below hyperscale cloud layer • $MSFT turning Azure into one of largest distribution layers for AI compute • $AKAM pushing AI inference closer to where users consume it • $AMZN monetizing AI through hyperscale cloud infrastructure & its own silicon • $ORCL building AI cloud capacity against a heavily concentrated backlog of large customer commitments Layer 8 | Model Execution, Tokens & Agents • $NET becoming distribution layer between AI agents and the internet as non human traffic crosses more than 50% of its network • $FSLY moving inference closer to the end user through edge compute

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• @StockSavvyShay neutral 890 eng

  THE ELECTRICITY VALUE CHAIN • $CCJ uranium supply • $CEG nuclear baseload • $SMR modular nuclear reactors • $OKLO micro-nuclear generation • $EOSE zinc long-duration storage • $GEV power conversion hardware • $VRT power distribution & cooling • $VST flexible generation & storage • $TSLA utility-scale lithium storage • $FSLR thin-film solar for hyperscale • $NNE utility-scale renewable buildout • $TLN North American renewable generation • $BWXT nuclear components & specialty fuel

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• @StockSavvyShay bullish 883 eng

  WHO GETS PAID TO TURN AI DATA CENTERS ON U.S. data centers above 100MW are set to explode this decade but power, interconnects and electrical infrastructure determine how much of that pipeline actually gets built: What gets the data center online • $GEV sits at one of hardest bottlenecks with a $176B backlog stretching into 2031 & equipment pricing already more than 20% above Q4 2025 • $VRT captures ~$3.8M per AI MW from grid to rack & is moving further upstream through UtilityInnovation Group adding behind the meter power that can bypass interconnection delays. • $BE attacks same problem differently with onsite fuel cells letting data centers generate power without waiting years for the grid. • $FPS sits between the substation & building through switchgear & transformers with utilization still around 30% against capacity being built to support as much as $5B of revenue. Who already owns the power • $IREN, $CIFR, $CORZ, $APLD & $WULF already control power and interconnects from their bitcoin infrastructure which is why IREN could go from 5MW of active AI capacity toward a 480MW target in roughly a year since the scarce asset is energized land. • $VST owns generation already connected to grid giving it direct exposure to rising data center power demand while others wait through interconnection queues. • $CEG pairs an already interconnected nuclear fleet with hyperscaler demand giving it one of cleanest ways to monetize 24/7 power scarcity through long term contracts. What gets paid inside the rack • $ON $NVTS become more important as AI moves toward 800V DC in 2028 since content per rack could rise from ~$15K toward ~$115K as more of the power conversion stack moves inside system. • $VICR is one of purest plays on converting and delivering power efficiently close to the processor as rack densities rise. The market can announce hundreds of new data centers but a ton of the value accrues to whoever controls the power, interconnects and electrical infrastructure required to make them operational.

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• @StockSavvyShay bullish 825 eng

  10 WAYS TO INVEST IN THE AI DATA CENTER BUILDOUT Data center demand is expected to nearly quadruple to 194 GW by 2035 as AI scales across more users, more tokens per user and more compute per token. Here are some of my favorite names positioned to benefit: 1. $TSM foundry manufacturing the accelerators, custom chips & networking silicon that nearly the entire AI buildout depends on. 2. $VRT liquid-cooling leader benefiting as rack density rises & AI systems require increasingly advanced thermal management. 3. $NVDA & $AMD accelerator duopoly at center of AI buildout with Nvidia's Vera Rubin entering full production & AMD’s Helios rack-scale systems emerging as a credible second source. 4. $MU & $SKHY memory engines of the AI era with HBM content scaling on every rack with data center margins above Nvidia's at segment level while suppliers themselves forecast shortage running into 2030. 5. $AVGO & $MRVL custom-silicon backbone, designing accelerators, networking chips & hyperscaler ASICs that capture more content across every AI rack. 6. $CRDO & $ALAB high-speed connectivity layer supplying the interconnect that lashes thousands of accelerators into one fabric 7. $OKLO & $SMR next-gen nuclear frontier with Oklo building the "local power plant" targeting microreactors beside data center campuses & NuScale developing larger modular reactors for utility-scale power. 8. $BE, $CEG & $VST provide power behind the AI buildout with Bloom delivering onsite fuel cells that bypass grid delays while Constellation & Vistra supply large-scale nuclear, gas & storage capacity to hyperscalers. 9. $LITE, $COHR & $AAOI optical layer benefiting from the transition from 800G to 1.6T with Lumentum & Coherent as components suppliers Nvidia took equity stakes in to secure next-generation optics capacity while AAOI is ramping U.S.-based 800G transceiver production for hyperscalers. 10. $ASML, $AMAT, $LRCX & $KLAC form equipment layer behind every new fab & packaging line with ASML’s EUV monopoly as ultimate chokepoint & Applied Materials, Lam & KLA supplying the deposition, etch & inspection tools required for every capacity expansion.

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• @cevikfinance bullish 501 eng

  Böyle sert düşen günler sağlam şirketlere uzun vadeli eklemeler yapmak için harika fırsatlar. İşte bu sağlam şirketlere bazı örnekler: $MU $AMD $TSM $GEV $CRDO $ALAB $MRVL $ASML $AVGO $CEG $VST $ANET $VRT $TER $FIX $PWR $CLS $LLY $PLTR $NBIS $SOFI $HOOD $APP $COHR $RKLB $GLW $LRCX $BE $DELL Yatırım tavsiyesi değildir, her zaman kendi araştırmanızı yapınız!

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• @StockSavvyShay bullish 493 eng

  Data center capacity is projected to add ~106GW from 2026 through 2030 reaching 174GW and implying more than $5T of total spending. That buildout could drive 50–60% of new U.S. power capacity through 2030 spanning $GEV turbines, $BE on-site fuel cells, $CEG and $VST generation and $VRT cooling and electrical infrastructure as hyperscalers work around years-long grid queues. Once the power is secured, spending flows into $NVDA and $AMD accelerators, $AVGO and $MRVL custom silicon and networking, $TSM manufacturing and HBM from $MU, $SKHY and Samsung. I also think as AI shifts toward more distributed and latency-sensitive inference in 2027 then demand should broaden beyond mega-campuses into edge and colocation facilities while $OKLO and $SMR offer longer-term nuclear optionality.

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• @TrendSpider bullish 473 eng

  $CEG breaking out following an almost half-million-dollar insider buy 💰 https://t.co/WJYMlMjx5G

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• @cevikfinance bullish 464 eng

  İleriye dönük fiyat kazanca göre en uzuz 10 şirket. $MU, $VST, $META, $NVDA, $ORCL, $ZETA, $CEG, $AVGO, $SOFI, $MSFT https://t.co/nSMGLAWZFx

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