$HPE 0.36 Noisy

Notable posts

• @amitisinvesting neutral 2064 eng

  A TON OF THINGS HAPPENED IN THE STOCK MARKET TODAY. Here's a full recap: 1. Broadcom $AVGO said it has a “high degree of confidence” it will ship $350B of AI semiconductors over the next 2 years, even if all 30 GW of demand is not deployed because of land, power, and data center constraints. The company laid out a massive AI growth path, targeting AI semiconductor revenue of roughly $58B in FY26, $115B in FY27, and $230B in FY28 — a 4x increase in just 2 years. Broadcom said every 1 GW deployed by OpenAI or Anthropic could support about $30B in annual AI revenue, while AI networking revenue is expected to grow just as fast as its XPU business. In Q3’26, Broadcom reported revenue of $29.6B, beating estimates of $29.36B and up 86% YoY, with adjusted EPS of $3.32 versus $3.24 expected. AI semiconductor revenue surged 221% YoY to $16.7B, and Broadcom expects that to accelerate to $21.7B in Q4, up 236% YoY, while also targeting $30+ EPS in FY28. 2. Meta $META rolled out Muse Spark 1.3, saying the latest model delivers its biggest coding and agentic performance jump yet. Meta says Muse Spark 1.3 matches GPT-5.6 Sol on Terminal-Bench 2.1 with a score of 88.8 and reaches 75.4 on DeepSWE. The model is available now through Muse Code and the API. Zuckerberg also teased Watermelon and open weights coming next, signaling Meta is continuing to push aggressively on frontier coding agents and open AI distribution. 3. Nvidia-backed neocloud Nscale is telling prospective IPO investors it has roughly $103B in contracted revenue, up from $51B before its $45B compute deal with Anthropic, according to The Information. The contracts average 5.7 years, implying about $18B of future contracted revenue per year, though the figure is not current revenue or formal guidance and was described by one source as “illustrative.” Actual revenue is still much smaller but scaling quickly, rising from about $37M in Q1 2026 to more than $100M in Q2, excluding the Anthropic deal, as Nscale prepares for an IPO that could come as soon as this month. 4. HPE $HPE expanded its Oracle $ORCL AI data center deal, with HPE set to deploy Juniper networking gear across Oracle AI data centers globally under a potential multi-year agreement. The deal covers routing, switching, networking support, and financing for OCI’s AI clusters, regional data centers, and edge networks, while HPE also issued Oracle warrants to purchase HPE shares. Separately, HPE reported Q3’26 revenue of $12.2B, beating estimates of $11.91B and up 34% YoY, with adjusted EPS of $1.11 versus $0.93 expected. Networking revenue surged 74.9% YoY to $2.9B, while Cloud & AI revenue rose 25.4% YoY to $9.0B. HPE guided Q4 revenue to $13.9B–$14.8B, above the $12.96B estimate, raised FY26 guidance, and said AI is becoming a multi-year growth driver as record backlog supports the outlook. 5. Cantor Fitzgerald initiated Tempus $TEM at Overweight with an $80 price target, arguing the company is being mispriced as a traditional life sciences data vendor rather than an AI-powered platform business. The firm says Tempus’ Data & Applications segment has a stronger growth and margin profile than the market is giving it credit for, with 26% growth and 76% gross margins. Cantor believes the segment should be compared more closely to platform peers like $PLTR, $SNOW, $DDOG, and $RDDT rather than slower-growing data-vendor peers, creating a favorable upside risk/reward. 6. Google $GOOGL launched Gemini 3.8 Flash Cyber, a new cybersecurity-focused AI model that scored 86.2% on CyberGym and 47.2% on CWE-Bench. Google says the model has a 70%+ success rate identifying vulnerabilities across 20 programming languages and produced 2.6x more correct patches than larger models on real Chrome security bugs. Initial access is limited to government agencies and cybersecurity partners through Google’s Fairwind program. 7. The top 10 most active options today by contracts traded were $NVDA with 5.7M contracts, $TSLA with 2.8M contracts, $AAPL with 1.7M contracts, $META with 1.0M contracts, $MU with 959K contracts, $AMZN with 732K contracts, $PLTR with 675K contracts, $DELL with 633K contracts, $INTC with 557K contracts, and $GOOGL with 536K contracts. 8. JPMorgan says a 5% 10-year Treasury yield could be the level that starts to pressure stocks, with Grace Peters noting that “5% psychologically has an impact” and could trigger a knee-jerk equity selloff. She sees a potential 5%-8% correction into the midterms, but views that as a healthy pullback rather than a structural break. JPMorgan remains constructive on stocks longer term, with its thesis centered on a capex-driven earnings supercycle, while the key medium-term test for AI will be whether the spending translates into real returns. 9. Berenberg initiated Rocket Lab $RKLB at Buy with an $83 price target, implying about 29%-35% upside, calling it the only end-to-end public pure-play in space across launch, satellites, components, and spectrum. The firm says Rocket Lab has an effective monopoly in dedicated small-lift launch, is entering medium-lift with Neutron, and is benefiting from rapid growth in satellite manufacturing and components as space budgets hit records. Berenberg also said the Iridium acquisition adds scarce global spectrum and recurring applications revenue, while Rocket Lab’s vertical integration gives it long-term optionality not fully reflected in the stock’s valuation. While acknowledging execution risk and a high multiple, the firm called Rocket Lab one of the most compelling long-term assets in the space sector and said it would buy or add on volatility. 10. The 60+ day delinquency rate on U.S. subprime auto loans has climbed to roughly 5.2%, the highest level on record and more than double where it was four years ago. Serious subprime auto delinquencies are now about 1.7 percentage points above their 2008 financial crisis peak, while prime auto loan delinquencies have also risen to around 0.4%, near the highest level since 2011. At the same time, total U.S. auto debt increased by $28B in Q2 2026 to a record $1.71T. 11. Onchain tokenized equity holders have reached a record 1.9M, up 134% month-over-month and 1,360% year-to-date. Just 10 months ago, fewer than 100,000 people held tokenized assets, but demand for 24/7 markets and access to names tied to the record IPO wave, including SpaceX, OpenAI, and Anthropic, has accelerated adoption. Jupiter, the largest onchain trading platform on Solana, has driven much of the growth, with 61% of volume now happening during off-hours and active tokenized equity traders up 46% month-over-month. 12. Microsoft $MSFT will begin disclosing Azure revenue as part of a major FY27 reporting overhaul, shifting from three business segments to two: Agents & Infrastructure and Devices & Consumer. For Q1 FY27, Microsoft expects Azure growth of 44%-45% in constant currency, Agents & Infrastructure revenue of $75.15B-$75.75B, Devices & Consumer revenue of $14.7B-$15.2B, Microsoft 365 Commercial Cloud growth of about 17% in constant currency, and Search & Ads ex-TAC growth in the mid-to-high single digits. The new structure marks Microsoft’s biggest reporting change since adopting its prior three-segment model in FY2016. WALL STREET IS THE GREATEST SHOW ON EARTH.

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• @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 1352 eng

  12 STRONGEST EARNINGS PRINTS THIS CYCLE 1. $PLTR | Palantir 2. $DOCN | DigitalOcean 3. $BE | Bloom Energy 4. $SITM | SiTime 5. $RKLB | Rocket Lab 6. $TE | T1 Energy 7. $AMD | AMD 8. $MU | Micron 9. $HPE | Hewlett Packard 10. $NBIS | Nebius 11. $SNDK | SanDisk 12. $DELL | Dell

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• @amitisinvesting bullish 1040 eng

  $SMCI Woah. SMCI guided for 8.4-8.7% gross margins last quarter. They have a TTM gross margin of 8.83%. They just came out after hours and said they are increasing their guide for gross margins to now 15-17%. Even during 2024-2025, their gross margins went from 11-13%. How does a commodity server rack provider double their margins in the past 90 days? If anything, it feels like the company is either rolling out new products with better margins in the AI space or gaining more leverage with their customers to command better margins. If a low margin business like $SMCI can gain leverage, then other AI names can as well, which is part of the reason that competitors like $DELL and $HPE are up after hours. Pretty incredible move from SMCI and could be a big moment for many AI names that are able to increase margins. $SMCI +15%

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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 734 eng

  $NVDA VERA RUBIN SUPPLY CHAIN Rubin is becoming a system-level buildout with rising content across memory, packaging, networking, power and cooling pushing the opportunity well beyond the GPU: • Memory sits with $MU, $SKHY & Samsung supplying the DRAM & HBM feeding Rubin accelerators while $SNDK adds exposure to storage layer around AI systems • Advanced packaging runs through $TSM, $AMKR & $INTC for foundry, packaging & assembly while $AMAT, $LRCX, $KLAC, $ASML, $TER & $AEHR provide equipment & testing needed to manufacture complex AI chips • Optical networking spans $COHR, $LITE & $AAOI in optical modules, $AVGO & $MRVL in switching silicon, $CRDO & $ALAB in high-speed connectivity & $GLW, $CIEN, $NOK & $ANET across fiber & network infrastructure • 800VDC power reaches from power semiconductors with $STM, $ON, $NVTS, $POWI & $WOLF into power management with $MPWR, $ADI, $TXN & $VICR • Power infrastructure & cooling sit downstream with $VRT, $ETN & $GEV supplying electrical distribution, backup power & thermal systems needed as rack densities keep rising • Compute systems bring full rack together through $DELL, $HPE & $SMCI which integrate Nvidia compute, networking, memory & power into deployable AI systems

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• @StockSavvyShay bullish 404 eng

  $HPE just landed a $1.2B Vultr order for $AMD Helios systems with Vultr saying AI demand “continues to outpace available capacity.” That gives AMD first real Helios proof point at scale while HPE gets a clean win as AI spending moves deeper into full rack systems. https://t.co/EF0BccxOtA

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• @Venu_7_ bullish 312 eng

  The index has gone sideways for three months. Most people see that as dead money. William O'Neil saw it as the most important research window of the cycle. While the market chops, the next leaders are quietly building bases. When the market turns, they are the first stocks out. Here is what to look for: - The stock is holding above its 50-day line while the index struggles - Its relative strength line is at or near new highs, meaning it is outperforming a flat market - It is forming a recognizable base near its highs: cup with handle, flat base, or double bottom - Volume dries up inside the base. The sellers are running out. - Earnings and sales growth are still accelerating underneath That is your watchlist for the next leg: $NBIS $SMTC $INTC $BE $GLW $CRM $NOW $PLTR $ABNB $FIVN $TEAM $CRWD $NET $TWLO $FROG $FTNT $PANW $RBRK $HPE $DELL $HPQ $ANET $APH $COHU $MRVL $AMD $NVDA $MU $SNDK $U $NTAP $FORM You do not need to predict when the market turns. You need to have the list ready for when it does. The index tells you when. The stocks holding up best tell you what.

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