$EQIX 0.57 Developing

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 961 eng

  WHO GETS PAID IN THE AI CLOUD STACK AI clouds are becoming the middle layer between scarce AI infrastructure and companies that actually need compute: Who supplies the AI clouds • $NVDA sits at center of compute layer by supplying the GPUs that determine how much capacity these platforms can actually bring online. • $MU & $SKHY supply HBM required to keep those GPUs fed as model sizes and inference workloads continue scaling • $DLR, $EQIX, $CORZ & $APLD provide physical data center infrastructure underneath the cloud layer giving AI clouds another path to scale without owning every building themselves. Who turns that infrastructure into compute • $CRWV, $NBIS & $IREN sit directly in middle of the stack by combining GPUs, power, networking and software into usable AI compute that customers can rent. Who buys the compute • $MSFT, $META & $GOOGL are unique because they are both customers and competitors by renting external capacity when internal supply is constrained while continuing to build their own • $SHOP & $CRWD show where next leg can come from as enterprise demand broadens customer base beyond a handful of hyperscalers and AI labs. • OpenAI & Anthropic represent AI lab customer base where demand can scale really quickly as training and inference requirements grow.

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• @wallstengine mixed 698 eng

  ROTHSCHILD REDBURN LAUNCHES COVERAGE ACROSS AI DATA CENTER NAMES $NBIS: SELL, $84 PT Says Nebius has a demanding valuation and questions the sustainability of its unit economics. Upside could come from newer-generation GPUs, faster growth of its Token Factory inference business and stronger capacity execution. Risks include customers bringing compute in-house and higher funding costs. $CRWV: SELL, $54 PT Redburn questions CoreWeave's unit economics and ability to convert its pipeline into attractive returns. Key risks include hyperscalers or AI labs bringing capacity in-house, falling GPU pricing and higher financing costs. $DLR: BUY, $227 PT Highlights Digital Realty's global wholesale and colocation footprint, long-term tenant contracts and expansion into larger, higher-density facilities built for AI workloads and hyperscale customers. $EQIX: BUY, $1,261 PT Points to Equinix's global interconnection ecosystem as a key advantage, with AI, HPC, enterprise and cloud customers increasingly needing high-density compute close to networks and other infrastructure. $IRM: BUY, $132 PT Iron Mountain has expanded beyond its legacy records-storage business into data centers, information management and IT asset lifecycle services, while developing more power-dense capacity for AI workloads. The miner-to-AI names were treated much more cautiously: $APLD: NEUTRAL, $22 PT Says unit economics and pipeline-conversion risks are already well priced in. Upside comes from lower build/operating costs and additional large tenant signings. Risks include local opposition, construction delays and tenant insolvency. $IREN: NEUTRAL, $40 PT Sees upside from additional large-scale AI tenants and better execution at existing sites. Risks include difficulty securing tenants, higher funding costs and delays or cancellations. $WULF: NEUTRAL, $15 PT Potential upside comes from securing additional U.S. capacity and signing more major tenants. Permitting issues and state-level opposition are key risks. $CIFR: NEUTRAL, $18 PT Sees better or faster grid-capacity allocation and more major tenant deals as upside. Tenant pullouts and buildout delays are the main downside risks. $HUT: NEUTRAL, $96 PT Upside depends on faster data-center execution and securing additional grid capacity. Delays or cancellations in the buildout remain the key risk. $CORZ: NEUTRAL, $16 PT Core Scientific has been shifting capacity from Bitcoin mining toward AI/HPC hosting after emerging from bankruptcy, while continuing its mining business. Its proposed acquisition by CoreWeave collapsed last year after shareholders rejected the deal.

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• @Ashton_1nvests bullish 71 eng

  My favorite stocks across the market right now: Semiconductors: $AMD Enterprise Software: $NOW Digital Advertising: $APP Fintech: $SOFI E-Commerce: $AMZN Mobility: $UBER Streaming: $NFLX Social Media: $SNAP Healthcare Insurance: $OSCR Alternative Assets: $BN Data & Marketing: $ZETA Utilities / AI Power: $VST Digital Infrastructure: $EQIX Consumer Internet: $DUOL Cybersecurity: $CRWD Cloud Software: $CRM Creative Software: $ADBE Social Platforms: $META Consumer Growth: $CELH Entertainment: $FUBO Payments: $V Traditional Finance: $JPM Energy: $XOM Materials: $FCX Retail: $WMT Some of these I own, some I watch, and some I would only buy at the right valuation. But if I had to pick one company from each corner of the market, this is pretty close to my list. What would you change?

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• @wallstengine neutral 66 eng

  Equinix $EQIX is expanding its collaboration with Cisco $CSCO and NVIDIA $NVDA to deploy secure AI factories across its global data center footprint.

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• @faststocknewss neutral 14 eng

  COATUE Q2 13F: MICRON UP 1794%, NEW $3.2B SPACEX AND $1.5B CEREBRAS STAKES Largest holdings: - Taiwan Semiconductor $TSM: $4.26B - Lam Research $LRCX: $4.09B - Micron $MU: $3.63B, shares +1794% - SpaceX: $3.17B, 18.6M Class A shares, new - Applied Materials $AMAT: $3.05B - GE Vernova $GEV: $3.01B - Amazon $AMZN: $2.82B, shares +49% - Broadcom $AVGO: $2.21B - Eaton $ETN: $2.12B - Alphabet $GOOGL: $1.74B - Intel $INTC: $1.69B, new - Cerebras Systems: $1.55B, new - Meta Platforms $META: $1.42B - Equinix $EQIX: $1.33B - Nvidia $NVDA: $1.21B - Constellation Energy $CEG: $1.15B - Forgent Power Solutions $FPS: $1.15B, new - Hut 8 $HUT: $1.12B, new - Microsoft $MSFT: $1.10B Other new positions: - Booz Allen Hamilton $BAH: $65.7M - Advanced Micro Devices $AMD: $55.8M - Comfort Systems $FIX: $35.3M - Argan $AGX: $28.8M Added: - Carvana $CVNA: +382% - Alphabet Class A: +13% - Microsoft: +18% - Equinix: +17% Exited: - Visa $V: $217.2M - Chime Financial $CHYM: $188.6M - Solstice Advanced Materials $SOLS: $126.0M - C.H. Robinson $CHRW: $95.2M - iShares Bitcoin Trust $IBIT: $2.6M Trimmed: - Synopsys $SNPS: -58% - ASML $ASML: -40% - Netflix $NFLX: -32% - Pinterest $PINS: -29% - Applied Materials: -20% - Alphabet Class C $GOOG: -19% - UiPath $PATH: -14%

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• @TENETTRADEGROUP neutral 20 eng

  Tenet Market Wake-Up Summary 📷📷 June.16.26 $SPX $HPE $QCOM $MSFT $NVO $LLY $OLN $HUN $AMD $RXT $MBLY $INTC $OKLO $HOOD $DELL $NVDA $EQIX $CSCO $PLAY $DOMO $WLY $XOM $CAMP $FLUT $DT $TSLA $PLTR $ROK $SKT $HITI $SHMD $SPCX @ripster47 | @tenet_research | @TenetCharts https://t.co/bRg3Ciza0Z

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