$CSCO 0.51 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 bullish 958 eng

  $NVDA AI dominance is creating a massive opportunity well beyond Nvidia itself as billions flow through foundry, HBM, testing, packaging and server assembly Here’s who has the most exposure to Nvidia today: • $TSM 37% • $SKHY 27% • $MU 14% • $TER 8% • $ASX 3% • $FN 1% • $AMKR 1% • $CSCO 1% I also think the wild part is that Nvidia now spends more on HBM than on the wafers themselves and roughly a quarter of its revenue equivalent on test equipment which says a lot about how important memory, yield and validation have become at these die sizes.

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• @StockSavvyShay neutral 747 eng

  THE $NVDA VERA RUBIN SUPPLY CHAIN BREAKDOWN Nvidia’s Vera Rubin is ramping into full-scale production and reshaping the AI rack bill of materials in three major ways: more HBM per rack, a shift from pluggable to co-packaged optics and the move to 800VDC power. Here are the companies positioned to capture each transition: Memory Storage (content-per-rack story) • $MU, $SNDK, $SKHY and Samsung sit at the memory and storage layer as next-gen AI systems require more bandwidth, more capacity and faster data access. Advanced Packaging (bottleneck that gates everything) • Foundry / packaging: $TSM, $AMKR, $INTC • Packaging equipment: $AMAT, $LRCX, $KLAC, $ASML • Test / validation: $TER, $AEHR • Substrates / materials: $TTMI Optical Communication (CPO transition) • Optical modules: $COHR, $LITE, $AAOI • CPO / switching chips: $AVGO, $NVDA, $MRVL • High-speed connectivity: $CRDO, $ALAB • Fiber / networking: $GLW, $NOK, $CSCO, $ANET • Silicon photonics / packaging: $TSM, $GFS, $TSEM 800VDC Power Supply (architecture shift) • Wide-bandgap semis: $STM, $ON, $NVTS, $POWI, $WOLF • Power management / hardened devices: $MPWR, $ADI, $TXN, $AOSL, $VICR, $MCHP • Motherboard / connector / power module: $FLEX, $APH, $TEL • Infrastructure / power delivery: $VRT, $ETN, $GEV Compute System (integrators) • $DELL, $HPE, $SMCI provide the system-level integration layer that turns all of these components into deployable AI infrastructure.

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• @StockSavvyShay bullish 707 eng

  $AMD and $CSCO have brought their first production AI infrastructure online in Saudi Arabia with 250 MW of MI400 capacity planned from 2027 and a path to 1 GW by 2030. That gives AMD a path to a multi-generation sovereign AI win. https://t.co/CleMggPP1d

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• @WOLF_Financial neutral 704 eng

  HERE ARE THE WORLD'S 50 MOST VALUABLE COMPANIES AS OF JUNE 2026 TECHNOLOGY (22 of the top 50): NVIDIA $NVDA: $4.8T Apple $AAPL: $4.3T Alphabet $GOOGL: $4.2T Microsoft $MSFT: $2.8T SpaceX $SPCX: $2.1T TSMC $TSM: $2.3T Broadcom $AVGO: $1.8T Samsung: $1.5T Meta $META: $1.4T SK Hynix: $1.2T Micron $MU: $1.2T AMD $AMD: $848B Intel $INTC: $665B ASML $ASML: $685B Arm $ARM: $391B Cisco $CSCO: $478B Oracle $ORCL: $476B Applied Materials $AMAT: $465B Lam Research $LRCX: $464B KLA $KLAC: $319B Tencent: $485B Kioxia: $313B CONSUMER DISCRETIONARY: Amazon $AMZN: $2.5T Tesla $TSLA: $1.4T Home Depot $HD: $324B ENERGY: Saudi Aramco: $1.7T ExxonMobil $XOM: $579B Chevron $CVX: $350B FINANCIALS: Berkshire Hathaway $BRK: $1.1T JPMorgan Chase $JPM: $895B Visa $V: $625B Mastercard $MA: $431B Bank of America $BAC: $411B Morgan Stanley $MS: $357B Goldman Sachs $GS: $323B China Construction Bank: $382B Agricultural Bank of China: $326B ICBC: $308B HSBC $HSBC: $328B CONSUMER STAPLES: Walmart $WMT: $950B Costco $COST: $425B Procter & Gamble $PG: $351B Coca-Cola $KO: $346B HEALTH CARE: Lilly $LLY: $987B Johnson & Johnson $JNJ: $576B AbbVie $ABBV: $415B UnitedHealth $UNH: $372B INDUSTRIALS: Caterpillar $CAT: $453B P&G $PG: $351B (also listed under consumer staples) GE $GE: $372B (Photo Credit: VisualCapitalist)

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• @TrendSpider bearish 258 eng

  Triple RSI not being shy $CSCO 🌶️ https://t.co/Ujf68gl4sK https://t.co/l7yqPZpPbE

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• @wallstengine bullish 210 eng

  CISCO $CSCO Q4’26 EARNINGS HIGHLIGHTS 🔹 Revenue: $17.3B (Est. $16.82B) 🟢; +18% YoY 🔹 Non-GAAP EPS: $1.22 (Est. $1.17) 🟢; +23% YoY 🔹 Adj. Oper Margin: 35.9% 🔹 Adj. Oper Income: $6.2B (Est. $5.85B) 🟢 Raises FY27 Guide: 🔹 Revenue: $72.2B-$73.4B (Est. $68.69B) 🟢 🔹 Adj. EPS: $5.05-$5.11 (Est. $4.80) 🟢 Q1 Guide: 🔹 Revenue: $18.0B-$18.2B (Est. $16.8B) 🟢 🔹 Adj. EPS: $1.32-$1.34 (Est. $1.16) 🟢 🔹 Adj. Gross Margin: 65%-66% 🔹 Adj. Oper Margin: 35.5%-36.5% Segment Revenue: 🔹 Networking: $9.79B; +28% YoY 🔹 Security: $2.23B; +14% YoY 🔹 Collaboration: $1.17B; +12% YoY 🔹 Observability: $275M; +6% YoY Other Q4 Metrics: 🔹 Product Orders: +35% YoY, +25% ex-hyperscalers 🔹 Networking Orders: +40% YoY, 8th straight double-digit qtr 🔹 AI Infra Orders: $4B in Q4, $9.3B in FY26 🔹 RPO: $46.7B; +7% YoY 🔹 Operating Cash Flow: $5.4B; +27% YoY Capital Return: 🔹 Dividend: $0.42/share 🔹 Returned $3.2B to shareholders in Q4 Comments: 🔸 “In fiscal 2026, Cisco achieved its highest productivity metrics in 30 years measured by revenue, non-GAAP operating margin, and earnings per employee.”

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• @TrendSpider bearish 251 eng

  Michael Burry compares Nvidia $NVDA in its current state to Cisco $CSCO during the dot-com bubble. He says Nvidia is even more concentrated than Cisco was; “NVIDIA is hurt badly if just one customer pulls back,” Do you agree? https://t.co/wC9rkLOccL

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