$ADI — FinTwit mentions on Surfaced

$ADI — FinTwit mentions on Surfaced

Recent mentions

42 mentions

@invesquotesJun 29, 2026 · 3:34 PM

6/ Senses & Nervous System: the analog signal chain. TI, ADI, Infineon. A few dollars of content per part so pretty easy to overlook, but similar switching costs that analog cos have historically experienced. A tollbooth on the hardware ramp-up. $TXN $ADI https://t.co/LadT9yLyOL

@invesquotesJun 20, 2026 · 6:42 PM

Analog semis were considered "dead" not many months ago. A lot of semis experts were claiming (while stocks were at 52wk lows) that management teams were running shareholders "off a cliff." Where are we today? - Most are at ATHs - They are starting to be considered bottlenecks - They are also being considered as main beneficiaries of the robotics era What's funny is that $TXN has been criticized for running with high inventory and expanding capacity but this is what ironically might help the company gain share through the shortage. This shows that it's not always the best to optimize for ST metrics (duh) Reminder that it's very easy to look intelligent when the stock price seems to be telling you that you are right $ADI

@investmentguru_Jun 18, 2026 · 1:13 AM

The Power Semiconductor Supercycle is REAL. While everyone chases AI software names — the hardware backbone is quietly printing. Here’s what the Power & Energy Semi leaderboard looks like: YTD returns ⚡ $NVTS +212% ⚡ $STM +182 % ⚡ $FLEX +136% ⚡ $POWI +127% ⚡ $AOSL +118% ⚡ $ON +110% ⚡ $VRT +90% ⚡ $TXN +75% ⚡ $MPWR +63% ⚡ $GEV +56% ⚡ $ADI +54% ⚡ $ETN +29% These aren’t meme stocks. These are the picks & shovels of the AI energy buildout. Every data center needs power delivery. Every EV needs power conversion. Every industrial system needs power management. ➡️ Semiconductors get the headlines. ➡️ Power semis do the actual work. The market is waking up — but this cycle is far from over. Not financial advice.

@kawzinvestsJun 7, 2026 · 8:12 PM

From 2023 to 2026 $NVDA kept adding more GPUs to each rack but the underlying power system stayed the same. Think of it like adding more appliances to your house without rewiring anything. You can do it up to a point. Rubin Ultra in 2027 hits that point. 576 GPU dies per rack pulling 600 kilowatts is physically impossible to run on the old wiring. So NVIDIA splits the rack into two separate units. One rack holds all the GPUs. A dedicated sidecar rack next to it handles all the power conversion at 800 volts DC. In the old system a 5kW power supply unit cost around $95 of semiconductor content. In the new 800V sidecar each unit is rated at 30kW and costs around $880 of semiconductor content. The per-unit cost goes up nearly 10x because the chips required to handle 800V cannot be made from regular silicon. You need silicon carbide and gallium nitride, which are more advanced and more expensive materials. Multiply that across an entire rack and you go from $20,186 of power semiconductors per rack at Rubin to $123,092 at Rubin Ultra. That gap does not go to $NVDA. It does not go to the companies building the racks. It goes entirely to the chip companies supplying the power semiconductors. Full report dropping Monday covering every name positioned for it. $IFX $MPWR $VICR $NVTS $STM $ON $TXN $ADI $POWI $DIOD $WOLF $AOSL

@bullofbritainJun 6, 2026 · 5:16 PM

UBS global research just put numbers on AI data center power semis. Here's a list of tickers they named specifically > Leaders: Infineon $IFX (Buy), $TXN > Incumbents: Renesas 6723.T, $ADI > Power stages / VRM: Monolithic $MPWR, Vicor $VICR > Wide band gap on the 800V shift: $NVTS, $STM, ROHM 6963.T, $ON > Smaller / laggard exposure: $POWI , $DIOD, $AOSL > PSU system level: Delta https://t.co/m1ww5mzgHD, Lite-On https://t.co/qN6lOAY0EA Content per rack is exploding: Hopper 23': $3.9k Blackwell 24': $12.6k Rubin 26': $20.2k Rubin Ultra 27': $123k Feynman 28': $202k 50x (FIFTY) in 5 years. Driver is the 800V DC shift forcing GaN/SiC across the stack, not unit volume. But UBS's own model implies 30GW+ of 2026 capacity adds vs 10-15GW consensus. Infineon's guide alone implies 40GW+. That is double. They predict the TAM $1.5BN 25' will jump to $3.8BN by 28' So chat, are there any power-semi plays not listed that you recommend I take a look at?

@thevalueistJun 1, 2026 · 10:52 AM

$MPWR $ADI $STM $TXN $NVTS $ON EXECUTIVE OVERVIEW The source material is best understood as an NVIDIA MGX 800 VDC power distribution board ecosystem display rather than a GPU, accelerator, or motherboard exhibit. The wall shows 11 named supplier groups participating in 800 VDC rack power delivery: Analog Devices, Delta, Infineon, Innoscience, Megmeet, Monolithic Power Systems, Navitas, onsemi, Renesas, STMicroelectronics, and Texas Instruments. The visible board set spans 800 V hot-swap protection and DC/DC conversion from 800 V to 50 V, 12 V, and 6 V, indicating that the industry is not converging on 1 immediate downstream rail but is instead preparing for a phased, multi-topology transition. NVIDIA frames 800 VDC as a response to the limitations of legacy 54 V rack-level distribution, with the stated objectives of reducing current, copper usage, cable bulk, conversion stages, energy loss, and compute-space power-delivery volume. (NVIDIA) The investment significance is that rack power has become a primary scaling constraint in AI infrastructure, not a secondary component category. The display is a physical manifestation of a platform-level architectural shift: power delivery is being pulled into the same ecosystem-control model as GPUs, networking, cooling, and rack mechanics. NVIDIA has stated that 800 VDC infrastructure is intended to support 1 MW IT racks and beyond starting in 2027, and that full-scale production of 800 VDC data centers is expected to coincide with Kyber rack-scale systems. (NVIDIA Developer) This points to a design-win cycle that is occurring well ahead of revenue recognition, with qualification, safety, reliability, and standards decisions likely to drive relative winners before reported financial contribution becomes visible. The display should not be interpreted as evidence that any 1 supplier has a protected monopoly socket. It argues the opposite. Multiple vendors are shown at each level of the power tree, including hotswap, 50 V conversion, 12 V conversion, and 6 V conversion. NVIDIA appears to be deliberately cultivating a broad, multi-sourced electrical ecosystem to avoid vendor concentration and accelerate availability of qualified designs. This is bullish for the overall AI power-delivery TAM, but it is more nuanced for individual equities: early validation can expand revenue optionality, while eventual open reference designs and multi-sourcing can compress scarcity premiums. TECHNICAL READ-THROUGH The architectural migration is driven by basic power physics. At 1 MW, an 800 V bus carries roughly 1,250 A before conversion losses. The same 1 MW at 54 V would require roughly 18,519 A, and at 48 V roughly 20,833 A. For a constant conductor resistance, I²R loss at 54 V is approximately 219x higher than at 800 V, and at 48 V approximately 278x higher than at 800 V. Real-world systems resize conductors, segment bus paths, and introduce converter losses, so the realized savings are not equal to the pure physics ratio. However, the direction and magnitude of the design pressure are decisive: low-voltage rack distribution becomes increasingly unattractive as racks move from 100 kW-class systems toward hundreds of kilowatts and ultimately 1 MW-class racks. NVIDIA quantifies the benefit of switching from 415 VAC to 800 VDC distribution as 85% more power transmitted through the same conductor size, 45% lower copper requirements, and up to 5% improvement in end-to-end efficiency versus current 54 V systems. (NVIDIA Developer) The image captures the likely transition architecture from current data center power distribution to native high-voltage DC. Today’s data center power chain generally involves medium-voltage utility input, step-down to low-voltage AC, UPS conditioning, AC distribution through PDUs and busways, rack-level AC/DC conversion into a 48 V or 54 V bus, and further conversion near compute trays and processors. NVIDIA’s 800 VDC vision centralizes AC/DC conversion at the facility level, distributes 800 VDC through the data hall, and shifts the critical conversion work to high-density DC/DC converters closer to the compute load. NVIDIA has described the Kyber-era MGX evolution as distributing high voltage directly to each compute node, where a high-ratio 64:1 LLC converter steps down to 12 VDC adjacent to the GPU; NVIDIA also states that this 1-stage conversion is more efficient and occupies 26% less area than traditional multi-stage approaches. (NVIDIA Developer) The visible rails on the display are especially important. 800 V-to-50 V is the least disruptive path because it preserves compatibility with the broad 48 V/54 V ecosystem and existing downstream voltage regulator designs. 800 V-to-12 V is a more aggressive topology that reduces intermediate conversion stages while still feeding a familiar voltage domain. 800 V-to-6 V is the most forward-looking path because it moves the bus voltage closer to the GPU core regulator input and can remove another conversion layer, but it also creates very high current density at the board level. STMicroelectronics explicitly states that 50 V, 12 V, and 6 V intermediate DC buses will coexist in AI data centers depending on rack density, GPU configuration, server form factor, and cooling strategy. (ST News) That is consistent with the display’s structure: this is not a 1-rail standardization event; it is a portfolio of voltage-transition options. The 6 V path should be viewed as high strategic optionality but not a guaranteed universal endpoint. Moving from 12 V to 6 V doubles current for the same power. A 20 kW load at 12 V requires roughly 1,667 A; the same 20 kW at 6 V requires roughly 3,333 A. That increases the importance of converter proximity to the accelerator, low-inductance packaging, busbar design, connector performance, thermal extraction, transient response, and fault isolation. The tradeoff is that 6 V can reduce conversion losses and improve voltage-regulator-stage efficiency when placed close enough to the load. Texas Instruments’ announced NVIDIA-aligned architecture uses only 2 conversion stages from 800 V to GPU core power: an 800 V-to-6 V isolated bus converter followed by a 6 V-to-<1 V multiphase buck converter, with the 800 V-to-6 V converter specified at 97.6% peak efficiency and more than 2,000 W/in³ power density. (Texas Instruments) Navitas separately disclosed an 800 V-to-6 V GaN-based board targeting 96.5% peak efficiency at full load, 1 MHz switching, and 2,100 W/in³ power density. (Navitas Semiconductor) SOURCE MATERIAL OBSERVATIONS The display’s supplier mapping is itself the core data. Analog Devices appears in 800 V hot-swap and 800 V-to-6 V. Delta appears in 800 V hot-swap plus 800 V-to-50 V and 800 V hot-swap plus 800 V-to-12 V. Infineon appears across 800 V hot-swap, 800 V-to-12 V, 800 V-to-50 V, and 800 V-to-6 V. Innoscience appears in 800 V-to-50 V. Megmeet appears in 800 V hot-swap plus 800 V-to-50 V, 800 V hot-swap plus 800 V-to-12 V, and a smaller 800 V-to-12 V implementation. Monolithic Power Systems appears in 800 V hot-swap plus 800 V-to-50 V, 800 V hot-swap plus 800 V-to-12 V, and 800 V-to-6 V. Navitas appears in 800 V-to-6 V. onsemi appears in 800 V-to-12 V. Renesas appears in 800 V-to-50 V and 800 V-to-12 V. STMicroelectronics appears in 800 V-to-50 V, 800 V-to-12 V, and 800 V-to-6 V. Texas Instruments appears in 800 V hot-swap, 800 V-to-12 V, and 800 V-to-6 V. The repeated presence of “hot-swap” labels is a key technical signal. High-voltage DC rack systems require safe insertion, removal, fault isolation, inrush-current management, arc mitigation, telemetry, and fast protection. At 800 VDC, hot-swap is not an ancillary control function; it is a core availability and serviceability requirement. Analog Devices emphasizes rack-level hot-swap protection, high-voltage conversion, digital control, telemetry, and scalable modules for 800 VDC AI infrastructure. (Analog Devices) Infineon describes the move to centralized 800 V HVDC as enabling power conversion directly at the GPU within the server board and highlights the need for silicon, SiC, and GaN expertise from grid to core. (Infineon) The presence of many hot-swap boards suggests that protection, monitoring, and controllability may be among the highest-value analog sockets in the architecture because failure at this layer can compromise rack uptime regardless of converter efficiency. The board-form-factor differences are also informative. Several 800 V-to-6 V examples appear as long, low-profile boards, consistent with a need for low-height, near-load integration. Several hotswap/50 V and hotswap/12 V examples appear larger and more heavily thermally managed, consistent with rack- or tray-level power conversion where heat dissipation and isolation dominate mechanical design. The display does not expose part numbers, power ratings, qualification status, or BOM composition, so exact revenue content cannot be inferred. However, the mix of small control boards, larger conversion modules, and long high-current boards indicates that the opportunity spans controllers, power stages, wide-bandgap devices, magnetics, isolation, sensors, firmware, packaging, busbars, connectors, and thermal interfaces. SUPPLIER AND COMPETITIVE IMPLICATIONS Analog Devices and Texas Instruments are positioned most clearly around analog control, protection, telemetry, and broad system-level power management. That is attractive because 800 VDC adoption increases the value of precision sensing, isolation, hot-swap control, fault detection, and digital telemetry. The challenge is that these companies are large, diversified analog franchises, so even meaningful early AI power growth may be diluted at the consolidated revenue level. TI’s disclosure of a complete 800 VDC solution, including hot-swap, 800 V-to-6 V, and 6 V-to-<1 V conversion, indicates a credible attempt to own more of the end-to-end reference design rather than only supplying discrete control ICs. (Texas Instruments) Infineon, STMicroelectronics, onsemi, and Renesas appear to be competing from the position of broad power semiconductor platforms. Their advantage is breadth across silicon, SiC, GaN, drivers, controllers, MOSFETs, protection, and automotive- or industrial-grade reliability processes. onsemi states that its portfolio addresses high-voltage AC/DC conversion, power supply units, 800 VDC distribution, and core power delivery, using silicon and SiC technologies with monitoring and control. (onsemi) Renesas highlights GaN FETs for faster switching, lower energy losses, thermal management, and up to 98% efficiency in LLC DCX-based DC/DC converters. (Renesas Electronics) This creates a credible multi-vendor field rather than a clear 1-company semiconductor bottleneck. STMicroelectronics is notable because its public positioning aligns closely with the rails displayed in the source material. ST states that its 12 V and 6 V architectures complement an existing 800 V-to-50 V solution and that its portfolio addresses 50 V, 12 V, and 6 V power distribution inside gigawatt-scale compute infrastructure. ST also frames the 6 V path as a means to reduce conversion stages, move the bus closer to the GPU, reduce copper usage, minimize resistive losses, and improve transient performance. (ST News) That is highly aligned with the visible board set and supports the conclusion that multiple rails will remain in play through the transition. Navitas and Innoscience represent higher-beta GaN exposure. Their strategic relevance increases if the industry shifts toward direct 800 V-to-6 V or ultra-high-frequency conversion stages that strongly favor GaN switching speed, power density, and lower switching loss. Navitas explicitly positions its 800 V-to-6 V board as eliminating the traditional 48 V intermediate bus converter stage, reducing conversion losses, freeing board space, and improving end-to-end efficiency. (Navitas Semiconductor) Innoscience claims full-link GaN coverage from 800 V input to GPU terminal conversion, spanning 15 V to 1,200 V devices, and argues that high-frequency GaN can shrink magnetics and improve power density. (InnoScience) The counterbalance is that high-beta GaN names face higher execution risk, qualification risk, reliability burden, and potential margin erosion if the ecosystem standardizes around multi-sourced reference designs. Delta and Megmeet represent the system-level and power-shelf layer. This layer may capture larger dollar content per rack than individual IC sockets, but typically with different margin structure, heavier manufacturing requirements, and more integration responsibility. Delta’s public disclosures are particularly relevant: it states that its 800 VDC grid-to-chip solutions include solid-state transformer conversion from medium-voltage AC to an 800 VDC bus, HVDC/DC power distribution boards enabling 800 V-to-12 V output at up to 98.5% efficiency, and 1.1 MW in-row power delivery to bridge legacy infrastructure with forward-looking rack designs. (Delta Americas) This underscores that the investment opportunity is not confined to semiconductor vendors; power shelves, in-row power, busway, cooling, and facility electrical equipment may capture significant economics. Monolithic Power Systems is strategically visible because the display shows it across 50 V, 12 V, 6 V, and hotswap-associated designs. That breadth supports its relevance to AI power architecture, but the display also weakens any simplistic view that MPS alone controls the NVIDIA AI power transition. The read-through is positive for TAM and validation, but mixed for moat if customers can qualify several suppliers across the same voltage rails. The key financial question is not whether MPS participates, but whether it sustains differentiated efficiency, integration, reliability, and customer qualification fast enough to defend premium gross margin as the ecosystem broadens. ARCHITECTURE AND TAM IMPLICATIONS The primary TAM driver is power density per rack, not just data center square footage or server count. When rack power moves from 100 kW-class levels toward 500 kW and 1 MW, the power-delivery content per rack scales dramatically. Each rack requires protection, isolation, conversion, telemetry, connectors, busbars, liquid-cooled or airflow-managed thermal paths, and facility integration. NVIDIA’s own discussion of legacy 54 V limitations highlights space constraints, copper overload, and inefficient conversions as racks exceed 200 kW, including a reference to 1 MW rack requirements. (NVIDIA Developer) This suggests that the AI power stack could become a larger attach-rate opportunity per GPU cluster even if accelerator ASPs eventually normalize. The economic value of efficiency is nontrivial at 1 MW-class density. A 5% power-efficiency improvement at a 1 MW rack equates to 50 kW of continuous avoided power draw. Over 8,760 hours, that is 438 MWh per rack-year. At $0.10 per kWh, the avoided electricity cost is approximately $43,800 per rack-year before demand charges, cooling leverage, PUE effects, downtime benefits, and capex offsets. At $0.15 per kWh, it is approximately $65,700 per rack-year. Across 1,000 1 MW racks, the same simple power-only math is $43.8 million to $65.7 million per year. This is why modest-looking efficiency percentages can justify material capex in hyperscale AI factories. NVIDIA cites up to 5% end-to-end power-efficiency improvement and up to 30% TCO reduction from gains in efficiency, reliability, and system architecture. (NVIDIA Developer) The transition also shifts value from server-level PSUs toward rack-, row-, and facility-level power conversion. This has 2 consequences. 1st, the power supply chain becomes more strategic and may be designed into AI clusters earlier, alongside GPU, networking, and liquid cooling decisions. 2nd, brownfield adoption is likely to be uneven. Some data centers may adopt in-row AC-to-800 V conversion to support 800 V racks without immediately rebuilding the entire electrical backbone. Others may move toward deeper facility-level DC distribution over time. NVIDIA’s ecosystem note states that the transition to fully realized 800 VDC architecture will occur in phases to give the industry time to adapt and the component ecosystem to mature. (NVIDIA Developer) RISK ASSESSMENT The most important caveat is that a booth display is not the same as a high-volume qualified production socket. The source material shows demonstrator boards and supplier positioning, not final BOM awards, pricing, volume commitments, reliability data, customer-specific qualification status, or service-life test results. For investment purposes, the distinction is critical. The image supports the existence of broad design activity; it does not prove revenue timing, share allocation, or gross margin durability. Safety and reliability remain gating factors. 800 VDC fault interruption is harder than lower-voltage DC or AC systems because high-energy DC arcs do not benefit from natural AC zero crossings. The system must handle inrush current, short-circuit current, connector touch safety, hot insertion, fault containment, telemetry, service workflows, and technician training. NVIDIA has acknowledged that facility-level VDC introduces challenges in safety, standards, workforce training, capex, opex, and deployment. (NVIDIA Developer) The prominence of hot-swap boards in the display should therefore be interpreted as a sign that protection and serviceability are central to commercialization, not peripheral features. Standards risk is also material. NVIDIA is pushing 800 VDC, but broad adoption requires interoperability across voltage ranges, connectors, rack mechanics, safety practices, power shelves, facility equipment, and monitoring systems. NVIDIA specifically points to Open Compute Project participation as important for interoperability, cost reduction, voltage-range alignment, connector interfaces, and safety practices. (NVIDIA Developer) A fragmented standards environment would slow deployment, increase qualification costs, and reduce near-term volume visibility for component suppliers. There is also a topology risk. The display includes 50 V, 12 V, and 6 V because no single rail is universally optimal across all rack densities, GPU generations, cooling designs, and server form factors. 50 V protects compatibility and may dominate transitional systems. 12 V reduces stages while preserving a more familiar power domain. 6 V maximizes proximity and conversion-stage reduction but creates extreme current-delivery requirements. The coexistence of these rails is positive for broad supplier opportunity, but it complicates forecasts because a supplier’s revenue content depends heavily on which rail is adopted in which platform generation. COMPETITIVE CONCLUSION The display is structurally bullish for AI infrastructure power content and for the strategic relevance of analog, power semiconductor, power module, and rack-power suppliers. It is not a clean single-name endorsement. The supplier count and rail diversity imply that NVIDIA is creating a competitive ecosystem with deliberate redundancy. In that framework, the most advantaged suppliers will likely be those able to deliver validated high-voltage protection, high-efficiency isolated conversion, wide-bandgap devices, thermal/mechanical integration, telemetry, manufacturability, and long-life reliability at scale. The strongest near-term investment read-through is that power delivery is becoming an AI platform-enabling category with earlier design-cycle visibility than traditional commodity power. The more cautious read-through is that many of the visible boards could be engineering demonstrations rather than production awards, and that open ecosystem development may limit sustained excess returns for any 1 supplier. The best risk-adjusted exposure is likely to favor companies that can monetize multiple layers of the stack across hot-swap, controllers, GaN/SiC power stages, modules, telemetry, and system integration, rather than companies dependent on a single conversion rail or a single demonstrator board. BOTTOM LINE The source material shows a tangible acceleration of the 800 VDC AI power ecosystem. Its core message is that megawatt-class AI racks require a new power architecture, and that NVIDIA is actively organizing a broad supplier base to make that architecture production-ready. The technical direction is clear: higher voltage distribution, fewer conversion stages, lower copper intensity, more telemetry, more hot-swap intelligence, higher power density, and closer conversion to the GPU. The investment conclusion is positive for the AI power-delivery value chain as a whole, but individual stock selection should be driven by evidence of production qualification, attach rate per rack, rail-specific adoption, reliability track record, and margin defensibility rather than by display presence alone.

@investmentguru_May 31, 2026 · 12:48 PM

Computex 2026 kicks off June 2nd. Theme: “AI Together.” $NVDA $AMD $INTC all expected on stage. But the real alpha isn’t on the mainstage — it’s in the supply chain sitting behind it. 👇 The AI chip war runs on optical bandwidth now. Optical Modules: $COHR $LITE $AAOI CPO Switch Chips: $AVGO $MRVL $NVDA AI Network Systems: $ANET $CSCO $CIEN $NOK Silicon Photonics foundries are the chokepoint nobody’s pricing in yet. Foundries: $GFS $TSEM $FN Advanced packaging is the quiet bottleneck of this entire cycle. Packaging Foundries: $TSM $ASX $AMKR Packaging Equipment: $AMAT $LRCX $ONTO $ASML Packaging Testing: $TER $FORM $COHU $AEHR Power delivery is the infrastructure layer everyone ignores until it breaks. Wide-Bandgap Semis: $STM $NVTS $ON Power Management: $ADI $TXN $MCHP $VICR Industrial Power: $VRT $ETN $GEV Computex isn’t just a tech show. It’s a live stress test of every link in the AI supply chain. Watch what gets announced. Watch what gets skipped. Both tell you something. Not financial advice.

@dillon_valdezMay 28, 2026 · 8:23 PM

The physical AI trade is going to tempt people into ranking robot demo videos. Wrong first filter. Start with the deployment stack: Actuators / motion control: $PH $RRX $TKR $AME $ST $ITT Sensors / vision: $CGNX $ZBRA $TDY $OUST $ON $ADI $AMBA Edge compute: $NVDA $QCOM $ARM $AMD $NXPI Simulation: $NVDA $SNPS $PTC $ADSK Factory automation / yield: $TER $ROK $EMR $HON $SYM Demos get attention. The supply chain decides who can actually deploy.

@benitozMay 26, 2026 · 1:56 AM

Power Is Everything BofA’s Vivek Arya just modeled the AI power semi market, and the cleanest read is the power split The 600kW+ rack pool is worth $0 today The megawatt rack does not exist yet Then 800V DC arrives CY26 $1.4bn CY27 $11bn, already the majority CY30 $16.1bn Zero to sixteen billion in five years, off a market that was not on a sellside model two years ago Analog ICs stay biggest, but SiC and GaN grow fastest at 63% and 69% The greenfield inverts $txn $adi $wolf $mpwr $on $stm $MCHP

@ftr_investorsMay 23, 2026 · 11:26 AM

The Future Investors presents the 30 quality stocks with the highest free cash flow margins — excluding stock-based compensation 📈🤑 1. $APP | AppLovin - 68.3% 2. $MA | Mastercard - 50.6% 3. $ANET | Arista Networks - 49.6% 4. $V | Visa - 47.1% 5. $MSCI | MSCI - 44.5% 6. $NVDA | Nvidia - 44.3% 7. $BAM | Brookfield Asset - 39.9% 8. $RMBS | Rambus - 39.0% 9. $PLTR | Palantir - 37.5% 10. $ADYEN | Adyen - 36.3% 11. $MELI | MercadoLibre - 36.2% 12. $ADBE | Adobe - 34.1% 13. $ADI | Analog Devices - 33.2% 14. $FICO | FICO - 32.7% 15. $SNDK | SanDisk - 32.2% 16. $BESI | BE Semiconductor - 30.9% 17. $FTNT | Fortinet - 30.2% 18. $AVGO | Broadcom - 29.9% 19. $VEEV | Veeva Systems - 28.6% 20. $KLAC | KLA Corporation - 28.3% 21. $INTU | Intuit - 27.3% 22. $TCEHY | Tencent - 26.3% 23. $CRM | Salesforce - 26.2% 24. $DXCM | DexCom - 26.2% 25. $ASML | ASML - 26.1% 26. $LCRX | Lam Research - 26.0% 27. $BSY | Bentley Systems - 25.8% 28. $TSM | TSMC - 25.7% 29. $AAPL | Apple - 25.6% 30. $HXSCL | SK hynix - 25.0% Source: @KoyfinCharts 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 - Revenue & EPS est. next 3Y >9% - Gross Profit Margin >40% - FCF Margin >25% - Altman Z Score > 2.8 - Marketcap >$10B Which of these 30 cash flow kings are in your portfolio? 👑🤑

@investmentguru_May 22, 2026 · 10:49 AM

Physical AI is the next wave. The software AI trade is maturing. The next capital cycle flows into machines that move, sense, and act in the physical world. Here’s the complete Physical AI stack — save this list THE BRAIN $NVDA → GPUs + Jetson processors + Omniverse digital twins + Isaac robotics stack PERCEPTION (LiDAR + Vision) $OUST → digital LiDAR for 3D robot mapping & navigation $AEVA → 4D LiDAR — distance AND velocity sensing $HSAI → solid-state LiDAR for autonomous robot perception $MBLY → Mobileye AI vision for robotic navigation $TDY → machine vision imaging for industrial robot guidance $AMBA → low-power AI chips for robotic computer vision $ADI → sensors + power chips for robot perception & actuation $SOUN → voice AI and conversational interfaces for human-robot interaction THE BODY (Robotics Platforms) $TSLA → Optimus humanoid robot $SYM → AI mobile robot fleets for warehouse automation $SERV → AI sidewalk robots for last-mile urban logistics $TER → collaborative robot arms for AI manufacturing $ZBRA → autonomous mobile robots for warehouse logistics $ROK → industrial automation platforms integrating AI robotics $PATH → UiPath — AI-powered automation orchestration connecting physical and digital workflows SURGICAL ROBOTICS $ISRG → da Vinci AI-assisted surgical robots $SYK → Mako AI robotic-arm for surgical navigation $PRCT → AquaBeam robotic systems for minimally invasive procedures The playbook: → Semiconductors enabled the software AI wave → Physical AI needs sensors, actuators, and edge compute at scale → This is the next infrastructure supercycle — just moving atoms, not bits Not financial advice.

@thevalueistMay 21, 2026 · 9:56 PM

$ADI KEY READ-THROUGHS FROM ANALOG DEVICES Q2 FY2026 EARNINGS CALL Analog Devices’ Q2 FY2026 call was a broad positive read-through for the higher-quality end of the semiconductor, AI infrastructure, industrial automation, automotive electronics, grid infrastructure, defense electronics, and medical technology ecosystems. The key market signal was that ADI’s recovery is no longer confined to inventory normalization or narrow restocking: revenue reached a record $3.62 billion, up 15% sequentially and 37% year-over-year; Industrial grew 20% sequentially and 56% year-over-year; Communications grew 22% sequentially and 79% year-over-year; data center grew more than 90% year-over-year; Automotive grew 8% sequentially; and channel inventory weeks declined while remaining within the company’s 6-week to 7-week target range. The most important broader-market conclusion is that B2B semiconductor demand has inflected across multiple verticals at the same time that AI-related demand is expanding from GPUs into power, optical, test, factory automation, grid infrastructure, and energy storage. The negative read-throughs are equally important: ADI’s gross margin appears near a local ceiling, consumer hardware is being pressured by sentiment, inflation, and memory choke points, and AI power-density innovation could displace portions of the legacy board-level power and passive-component value chain over time. Source material: Analog Devices Inc Q2 2026 earnings call transcript. HIGH-PERFORMANCE ANALOG AND MIXED-SIGNAL SEMICONDUCTORS (READ-THROUGH 1) Source commentary/data point: ADI stated that it is seeing “record demand for our products and solutions,” reported record revenue and EPS, and noted that “all of our industrial businesses increased sequentially and year-over-year.” Management also emphasized that channel inventory weeks declined while remaining within the 6-week to 7-week target range, and that Q3 should show “continued above-seasonal growth across industrial, automotive and communication.” Affected companies: Texas Instruments (TXN: US), Microchip Technology (MCHP: US), NXP Semiconductors (NXPI: US), Infineon Technologies (IFX: Germany), STMicroelectronics (STM: US), Renesas Electronics (6723: Japan), Arrow Electronics (ARW: US), Avnet (AVT: US). Directional impact and magnitude: Positive, medium-high for diversified analog/mixed-signal semiconductors; positive, medium for semiconductor distributors with industrial and automotive exposure. Transmission mechanism: ADI’s results indicate that the analog/mixed-signal recovery is becoming demand-led rather than channel-led. The combination of record revenue, broad end-market growth, declining channel inventory weeks, and above-seasonal Q3 guidance reduces the probability that the sector is simply benefiting from temporary distributor restocking. This is most relevant for companies with similar exposure to long-cycle industrial, automotive, communications, and embedded control demand. Texas Instruments, Microchip, NXP, Infineon, STMicroelectronics, and Renesas should benefit through improved order rates, higher factory utilization, stronger distributor turns, and less inventory overhang. Arrow and Avnet should benefit from improved sell-through and broader customer replenishment, particularly in industrial and automotive franchises. Near-term trading catalyst versus longer-duration shift: The near-term catalyst is positive estimate revision risk across analog and mixed-signal peers as investors extrapolate ADI’s clean-channel recovery into the broader group. The longer-duration shift is a potential transition from the 2023-2025 analog inventory correction into a new industrial/automotive/AI infrastructure content cycle. The read-through is strongest for premium analog franchises and weaker for commodity analog suppliers, because ADI’s management repeatedly highlighted unusually high ASPs, design-win stickiness, and product differentiation. ANALOG PRICING POWER AND DOWNSTREAM OEM COST INFLATION (READ-THROUGH 2) Source commentary/data point: Management said ADI “increased prices during the course of this year” to offset input-cost inflation, that pricing actions will add “a couple of points” to FY2026 growth, and that the company’s ASPs are “4x, 5x the industry average.” Management also stated that once ADI captures a design win, “competitive substitution is effectively zero.” Affected companies: Texas Instruments (TXN: US), Microchip Technology (MCHP: US), Monolithic Power Systems (MPWR: US), NXP Semiconductors (NXPI: US), Infineon Technologies (IFX: Germany), Tesla (TSLA: US), BMW (BMW: Germany), Siemens (SIE: Germany), Schneider Electric (SU: France), Rockwell Automation (ROK: US). Directional impact and magnitude: Positive, medium for premium analog suppliers; negative, low-to-medium for downstream OEMs with high embedded semiconductor content. Transmission mechanism: ADI’s commentary supports the view that premium analog and mixed-signal suppliers retain meaningful pricing power even after the post-COVID inventory correction. The “effectively zero” substitution comment is particularly important because it implies that OEM customers have limited practical ability to redesign high-value analog content out of platforms once those platforms enter production. This supports gross-margin resilience for differentiated semiconductor suppliers but creates cost pressure for downstream OEMs in autos, industrial automation, power systems, and high-end electronics. Tesla, BMW, Siemens, Schneider, and Rockwell are not necessarily uniquely exposed to ADI, but they represent end markets where higher analog/power/sensing content and supplier pricing discipline can raise bill-of-materials pressure. Near-term trading catalyst versus longer-duration shift: Near term, the read-through supports margin and pricing assumptions for premium analog names, especially where investors feared pricing giveback after the inventory correction. Longer term, the implication is that value continues to migrate toward suppliers that own hard-to-substitute analog, signal-chain, power, and sensing sockets. The negative downstream impact should be treated as margin-dilutive rather than demand-destructive, given that semiconductor content is generally enabling higher-value product functionality. ANALOG GROSS-MARGIN CEILING AND LIMITED INCREMENTAL UTILIZATION UPSIDE (READ-THROUGH 3) Source commentary/data point: ADI reported Q2 gross margin of 73% and operating margin of 49%, but management guided to a roughly 50 bps gross-margin decline in Q3 due to the absence of a one-time channel repricing benefit. Management said utilization should be “fairly neutral” and that “we don’t see a ton of future upside on gross margin from utilization given where we’re running the factories today.” When asked whether Q3 gross margin represented a local peak, management answered, “Yes. I actually think that’s the right way to think about it.” Affected companies: Analog Devices (ADI: US), Texas Instruments (TXN: US), Microchip Technology (MCHP: US), NXP Semiconductors (NXPI: US), ON Semiconductor (ON: US), Renesas Electronics (6723: Japan). Directional impact and magnitude: Negative, medium for near-term multiple expansion in analog semiconductors; neutral-to-slightly negative for incremental EPS leverage assumptions. Transmission mechanism: ADI’s demand commentary was very strong, but the margin commentary indicates that incremental upside may increasingly need to come from volume growth and mix rather than additional factory-utilization leverage. This matters because analog stocks often re-rate early in recoveries as investors underwrite both revenue acceleration and utilization-driven gross-margin expansion. If ADI’s utilization is already near a level where incremental gross-margin leverage is limited, the broader analog sector may face a cleaner revenue recovery but less dramatic earnings leverage than prior upcycles. This read-through is particularly important for names where investor models embed a large utilization-driven gross-margin snapback. Near-term trading catalyst versus longer-duration shift: Near term, the read-through is a constraint on analog-stock multiple expansion after a strong cyclical rally. Longer term, the implication is not structurally bearish; it indicates that high-quality analog franchises can sustain very high margins, but incremental earnings upside must be driven by higher-value mix, pricing discipline, and content growth rather than simply fuller fabs. AI DATA CENTER POWER AND OPTICAL INFRASTRUCTURE (READ-THROUGH 4) Source commentary/data point: ADI disclosed that data center now accounts for more than 75% of Communications revenue and grew more than 90% year-over-year. Management said the growth was “fueled pretty much equally by similar growth rates across both the power and optical portfolios,” that both portfolios had “strong orders and strong results,” and that data center is expected to be ADI’s fastest sequential grower in Q3. Management also said Communications, including data center, is an above-corporate-average gross-margin business. Affected companies: Monolithic Power Systems (MPWR: US), Vicor (VICR: US), Broadcom (AVGO: US), Marvell Technology (MRVL: US), Coherent (COHR: US), Lumentum (LITE: US), Astera Labs (ALAB: US), NVIDIA (NVDA: US), Advanced Micro Devices (AMD: US). Directional impact and magnitude: Positive, high for AI power and optical-exposed semiconductor suppliers; positive, medium for broader AI accelerator and connectivity suppliers. Transmission mechanism: The call confirms that AI infrastructure demand is pulling through high-value non-GPU content at the rack and board level. Power and optical grew at similar high rates, indicating that AI capex is not merely a GPU phenomenon; it is creating demand for voltage regulation, power management, optical control, optical interconnect, signal integrity, and high-speed connectivity. Monolithic Power and Vicor benefit through AI power-density demand. Broadcom, Marvell, Coherent, Lumentum, and Astera Labs benefit from the same AI connectivity and optical-capacity cycle. NVIDIA and AMD benefit indirectly because the power/optical strength validates continued AI rack deployment and rising system complexity around accelerators. Near-term trading catalyst versus longer-duration shift: Near term, the read-through is positive for AI infrastructure suppliers ahead of their own reports because ADI’s data center strength was both optical- and power-led, with strong orders and Q3 sequential acceleration. Longer term, it supports the thesis that the AI server value chain will continue broadening beyond accelerators into power delivery, optical networking, rack-scale interconnect, and system-level efficiency. VERTICAL POWER DELIVERY AND PASSIVE-COMPONENT DISPLACEMENT RISK (READ-THROUGH 5) Source commentary/data point: ADI’s planned acquisition of Empower Semiconductor was described as adding “the final piece” of a “grid-to-core power platform.” Management said Empower provides integrated voltage regulator technology and silicon capacitors, enabling “true vertical power delivery,” shrinking customers’ power footprint by up to 4x, reducing data center compute power consumption by an estimated 10% to 15%, and eliminating the need for “bulky external components.” Management also said meaningful Empower-related revenue is expected in 2027, not immediately. Affected companies: Analog Devices (ADI: US), Monolithic Power Systems (MPWR: US), Vicor (VICR: US), Murata Manufacturing (6981: Japan), TDK (6762: Japan), Yageo (2327: Taiwan), Vishay Intertechnology (VSH: US). Directional impact and magnitude: Positive, high for ADI over the longer duration; mixed for Monolithic Power and Vicor, with positive near-term demand but negative medium longer-duration competitive risk; negative, medium longer-duration for passive-component suppliers exposed to board-level power architectures. Transmission mechanism: Empower signals that power delivery is moving closer to the XPU, GPU, and CPU. This matters because the traditional power architecture relies heavily on board-level voltage regulation and external passive components. If integrated voltage regulators and silicon capacitors become more important in AI accelerator platforms, value may migrate from discrete board-level components toward integrated power-delivery solutions closer to the compute die. Murata, TDK, Yageo, and Vishay face potential displacement risk where bulky external passive content is reduced. Monolithic Power and Vicor benefit from the same power-density demand in the near term but face a more competitive long-term architecture battle if ADI successfully scales Empower’s IVR technology. Near-term trading catalyst versus longer-duration shift: The near-term trading impact should be limited because ADI said Empower is only post-revenue today and not material immediately. The longer-duration implication is more important: AI power delivery may become an integrated, proprietary, architecture-defining battleground by 2027, and companies with differentiated power density, transient response, and near-core voltage regulation should command higher strategic value. ATE AND ELECTRONIC TEST AND MEASUREMENT (READ-THROUGH 6) Source commentary/data point: Management said ADI’s data center and ATE businesses are “taking advantage of strong AI-driven infrastructure investments to achieve new highs” and are on “steep growth trajectories,” with increasing confidence in growth into 2027. ADI also said ETM supports end-to-end product development from R&D prototyping, debugging, and validation through mass production in AI, EVs, and secure communications, and that shrinking innovation cycles are propelling ADI’s design pipeline. Affected companies: Advantest (6857: Japan), Teradyne (TER: US), Keysight Technologies (KEYS: US), FormFactor (FORM: US), Chroma ATE (2360: Taiwan), National Instruments owner Emerson Electric (EMR: US). Directional impact and magnitude: Positive, high for AI ATE leaders; positive, medium-high for broader test, measurement, validation, and probe-card suppliers. Transmission mechanism: ADI’s ATE and ETM commentary reinforces a high-value test-intensity thesis. AI accelerators, advanced packaging, high-bandwidth memory interfaces, EV power electronics, ADAS systems, and secure communications all increase design complexity and verification requirements. That complexity raises demand for semiconductor testers, probe interfaces, RF/mixed-signal instrumentation, validation tools, and production test capacity. Advantest and Teradyne are the clearest beneficiaries from ATE demand tied to AI chip production. Keysight, Chroma, Emerson/National Instruments, and FormFactor benefit from broader validation, measurement, and probe-card intensity. Near-term trading catalyst versus longer-duration shift: Near term, ADI’s “new highs” and “steep growth trajectories” language should support positive order-read-throughs into ATE and instrumentation earnings. Longer term, the shift is structural because AI and EV platforms are increasing test content per device and expanding the scope of validation from silicon to system-level performance. INDUSTRIAL AUTOMATION, DIGITAL FACTORIES, AND ROBOTICS (READ-THROUGH 7) Source commentary/data point: ADI said automation is benefiting from “onshoring of advanced manufacturing” and “evolving labor dynamics,” which are increasing demand for digital factories and next-generation robots. Management said robots are becoming a larger share of factory investments, that ADI’s higher-value products are aiding automation’s “fast recovery,” and that humanoids and advanced robotics are increasing the opportunity pipeline. Affected companies: Rockwell Automation (ROK: US), Siemens (SIE: Germany), ABB (ABBN: Switzerland), Schneider Electric (SU: France), Keyence (6861: Japan), Fanuc (6954: Japan), Yaskawa Electric (6506: Japan), Cognex (CGNX: US), TE Connectivity (TEL: US). Directional impact and magnitude: Positive, medium-high for industrial automation leaders; positive, medium for sensors, connectors, machine vision, motion-control, and robotics suppliers. Transmission mechanism: ADI’s automation commentary indicates that industrial automation is recovering cyclically while also benefiting from secular onshoring, labor scarcity, semiconductor-fab automation, biopharma automation, data-center automation, and robotics penetration. This should support factory automation hardware, PLCs, servo systems, sensors, industrial networking, machine vision, and robotics demand. Rockwell, Siemens, ABB, Schneider, Keyence, Fanuc, Yaskawa, and Cognex are direct beneficiaries of higher automation capex. TE Connectivity benefits through higher connector and sensor content in industrial systems. Near-term trading catalyst versus longer-duration shift: Near term, the read-through is positive for industrial automation names where investors remain concerned about sluggish PMI data or delayed capex. Longer term, the more important implication is that automation is moving toward autonomy, raising semiconductor, sensing, connectivity, and edge-compute content per machine and per factory cell. AEROSPACE, DEFENSE ELECTRONICS, AND NATIONAL SOVEREIGNTY (READ-THROUGH 8) Source commentary/data point: ADI said aerospace and defense reached a new revenue high and that “increased focus on national sovereignty concerns” is accelerating an already strong multiyear growth path. Management also noted that Industrial, which includes ATE and aerospace and defense, is its most profitable business and has 15-year to 20-year average product life cycles. Affected companies: Mercury Systems (MRCY: US), Teledyne Technologies (TDY: US), Curtiss-Wright (CW: US), L3Harris Technologies (LHX: US), RTX (RTX: US), BAE Systems (BA.: UK), Leonardo (LDO: Italy). Directional impact and magnitude: Positive, medium for defense electronics, RF/mixed-signal, sensing, and mission-critical hardware suppliers. Transmission mechanism: ADI’s commentary confirms that defense electronics demand is being driven by national sovereignty, not just near-term budget cycles. This supports suppliers of secure communications, sensors, RF systems, mission computers, avionics electronics, power systems, ruggedized computing, and signal-processing hardware. Mercury, Teledyne, Curtiss-Wright, and L3Harris have particularly relevant exposure to defense electronics and mission-critical systems. RTX, BAE, and Leonardo benefit at the prime and systems level through sustained procurement and modernization demand. Near-term trading catalyst versus longer-duration shift: Near term, this supports earnings-revision confidence for defense electronics suppliers. Longer term, the implication is more durable because 15-year to 20-year product life cycles and sovereignty-driven procurement can create long backlog visibility, high switching costs, and structurally resilient margins. AUTOMOTIVE SEMICONDUCTORS, ADAS, AND BMS (READ-THROUGH 9) Source commentary/data point: ADI reported Automotive revenue up 8% sequentially and 2% year-over-year. Management said ADI is seeing content and share gains in next-generation ADAS and infotainment systems, with increased demand for GMSL, functionally safe power, and A2B. BMS revenue grew year-over-year for the first time in 2 years and was up double-digits year-over-year. Management also cited a material late-quarter pickup in China, record automotive performance in Europe and Japan, record bookings, positive book-to-bill, lean customer inventory, and expected L3 ADAS deployment in some China vehicles by year-end. Affected companies: NXP Semiconductors (NXPI: US), Infineon Technologies (IFX: Germany), STMicroelectronics (STM: US), ON Semiconductor (ON: US), Renesas Electronics (6723: Japan), Mobileye Global (MBLY: US), Aptiv (APTV: US), Denso (6902: Japan). Directional impact and magnitude: Positive, medium-high for automotive semiconductor suppliers; positive, medium for ADAS and Tier 1 electronics suppliers. Transmission mechanism: ADI’s automotive result supports 2 distinct positive read-throughs. First, the automotive semiconductor inventory correction appears to be largely complete in several higher-value sockets, especially BMS. Management explicitly said customers appear “fairly lean” after digestion and that ADI is not seeing inventory build-up. Second, growth is being driven by content and share gains rather than vehicle units. This is especially important for NXP, Infineon, STMicroelectronics, ON Semiconductor, Renesas, and Mobileye, where investor focus is shifting from global auto production units to EV, BMS, ADAS, safety, power, and domain-controller content per vehicle. Near-term trading catalyst versus longer-duration shift: Near term, this is positive for auto-semiconductor estimate revisions, particularly for companies exposed to China EVs, Europe/Japan production, BMS, and ADAS. Longer term, the L3 ADAS and BMS commentary supports a secular content-per-vehicle thesis even in a muted global auto unit environment. The important caveat is that management still said China was declining sequentially overall, so the read-through is strongest for semiconductor content growth and late-quarter momentum, not for a broad auto-unit recovery.

@blackpanthercapMay 21, 2026 · 5:44 PM

The clearest sign you can get 😳 “Physical AI is the next wave.” — Jensen Huang. Here’s the complete Physical AI list - save it: $NVDA: GPUs, Jetson processors, Omniverse digital twins and Isaac stack for physical AI robots.  $OUST: digital LiDAR sensors for robot 3D mapping and navigation.  $TSLA: Optimus humanoid robots for general-purpose physical tasks.  $SYM: AI mobile robot fleets for warehouse automation.  $SERV: AI sidewalk robots for last-mile urban logistics.  $TER: collaborative robot arms for AI manufacturing automation.  $CGNX: machine vision cameras for industrial robot perception.  $AMBA: low-power AI chips for robotic computer vision.  $AEVA: 4D LiDAR sensors for robot distance, velocity and mapping.  $HSAI: solid-state LiDAR sensors for autonomous robot perception.  $ISRG: AI-assisted da Vinci surgical robots for precision physical procedures.  $ZBRA: AI autonomous mobile robots for warehouse logistics automation.  $ROK: industrial automation platforms integrating AI robotics control.  $ADI: sensors and power chips for robot perception and actuation.  $ONDS: unmanned robotic systems for aerial and ground physical AI.  $SYK: Mako AI robotic-arm systems for surgical navigation.  $PRCT: AquaBeam robotic systems for minimally invasive prostate procedures.  $LAZR: Luminar LiDAR sensors for 3D robot and autonomy perception.  $MBLY: Mobileye AI vision systems for robotic navigation.  $TDY: machine vision cameras and imaging for industrial robot guidance. -BP Please note: this is not financial advice.

@invesquotesMay 21, 2026 · 8:23 AM

Analog companies have gone BRR lately, but if physical AI comes soon, they'll most likely go BRR BRR or even BRR BRR BRR? $TXN $ADI

@investmentguru_May 12, 2026 · 9:43 AM

The “Inflation Era” of AI Compute is Breaking Out Across the Board Every layer of the AI infrastructure stack is seeing demand explosion — and the winners aren’t just $NVDA. Here’s the full supply chain map 👇 ⚙️ FOUNDATIONAL INFRASTRUCTURE → PCB: $TTM, $JBL → CCL: $ROG → MLCC: $VSH → Liquid Cooling & Thermal: $VRT 🔴 CORE COMPUTE & MEMORY AI Silicon: → $NVDA $AVGO $AMD $INTC Memory / Storage: → $MU $SNDK $WDC $STX $INTC Power Management / Analog: → $TXN $ADI $NXPI $STM $MPWR $VICR Wafer Foundry: → $TSM $GFS $UMC Advanced Packaging / OSAT: → $TSM $ASX $AMKR 🔵 OPTICAL COMMUNICATIONS NETWORK Optical Components: → $LITE $COHR $AAOI Optical Fiber & Cable: → $GLW Silicon Photonics Foundry: → $TSEM $GFS INP: → $AXTI Optical DSP / Interconnect Silicon: → $MRVL $FN ☁️ CLOUD & AI PLATFORMS → $AMZN $GOOG $BABA $BIDU The AI compute supercycle isn’t one stock — it’s an entire ecosystem repricing in real time. Not financial advice. DYOR.

@investmentguru_May 9, 2026 · 3:53 PM

The Complete Semiconductor Playbook — AI Supercycle 2026 The AI trade is no longer one ticker. It’s a full stack. Here’s every layer: 💾 MEMORY $MU · $WDC · $STX · $SNDK · $AMAT · $LRCX → HBM4 = the new oil. Not a commodity anymore. Strategic infrastructure. 🔌 CONTROLLERS $RMBS · $SIMO · $ATOM · $MCHP · $ADI · $TXN → The traffic cops of every AI system. Overlooked. Undervalued. Critical. 📦 PACKAGING / TESTING $AMKR · $FORM · $CAMT · $ONTO · $COHU · $ACMR → CoWoS. HBM stacking. Chiplets. This is where chips become systems. 🧠 NEXT-GEN MEMORY $MRAM · $GSIT · $NVEC · $NLST · $CEVA → Beyond DRAM. Early-stage. Asymmetric upside when the cycle arrives. 🌐 OPTICAL / INTERCONNECT $LITE · $AAOI · $COHR · $CIEN · $MRVL · $GLW · $MTSI · $TSEM · $POET · $LWLG · $FN → The hottest sub-sector in semis right now. 800G → 1.6T. CPO is the next arms race.

@stocksavvyshayMay 9, 2026 · 3:15 PM

10 WAYS ROBOTICS IS GETTING BUILT OUT ACROSS SECTORS 1. $NVDA infrastructure layer for robotics stack where humanoid robots from Optimus to Atlas can train in Nvidia’s Isaac Sim virtual environment before ever taking a real step 2. $PLTR & $ORCL turn robot fleet sensor data into operational intelligence while $PANW & $CRWD secure robot nervous system as fleets scale & attack surface grows 3. $GOOGL, $MSFT, $META & $BIDU provide foundation models that give robots ability to reason, see & act 4. $ARM, $SNPS & $CDNS design chips that power robot brains while $TSM manufactures silicon & $INTC contributes both design & fabrication capacity for edge compute 5. $AMZN, $AAPL, $XPEV & $BABA are building competing robotics ecosystems across consumer, logistics & automation applications 6. $HON & $ROK provide industrial automation & control systems that integrate robots into existing factory infrastructure at scale 7. $MP supplies the rare-earth magnets that power motors & actuators throughout the robot supply chain 8. $QCOM Dragonwing chips handle 5G connectivity & on-device AI for robot coordination while $MBLY & $AMBA provide vision & compute silicon enabling real-time perception 9. $ADI, $TXN, $ON, $STM & $OSS sit closer to physical edge of robotics from analog chips that convert sensor signals, manage power & control motors to rugged AI compute systems that process data in real time 10. $TSLA only company attempting humanoid robots at manufacturing scale with first-generation Optimus production lines being installed at Fremont right now designed for 1M robots/year & Gigafactory Texas being built out for 10M robots/year at a $20-30K target price

@thevalueistMay 8, 2026 · 4:30 PM

$ADI another all-time high today. Outstanding looking breakout chart. I'm still long 1/21/28 calls. https://t.co/KSKGpHADrK https://t.co/6BO2JkjY0E

@leveraged_catApr 22, 2026 · 10:32 PM

I knew very little about the analog/power semis before the charts started screaming at me to pay attention Lesson in that $TXN $ADI

@thevalueistApr 22, 2026 · 9:47 PM

$ADI positive post move on $TXN print read through. https://t.co/vdMPouHn3f

@investmentguru_Apr 19, 2026 · 8:41 PM

$ADI — Analog Devices Sensor fusion is the nervous system of every robot. ADI makes the precision analog chips that convert real-world signals (pressure, motion, temperature) into data robots can act on. ADI stands out for resilient profitability, dividend safety, and growth exposure to robotics.

@mrmikeinvestingApr 19, 2026 · 4:01 PM

The upcoming $2T SpaceX IPO is about to cause a generational rally amongst the full space economy… Your chance to enter these space sector names below before they squeeze will end soon: Spacecraft & Launch Systems - $RDW, $RKLB, $FLY, $LUNR, $YSS Satellite Operators & Services - $AMZN, $ASTS, $SPIR, $PL, $IRDM Raw Materials & Mining - $FCX, $TECK, $MP, $AA Specialty Materials & Alloys - $ATI, $GLW, $MTRN, $CRS, $HXL Electronics and semiconductors - $NVDA, $LITE, $STM, $COHR, $ADI Don’t miss out on these potential life changing investment opportunities…

@investmentguru_Apr 19, 2026 · 11:51 AM

Physical AI play Silicon / Compute (The Brain) $NVDA — Owns the full AI stack $ARM — Royalties on every advanced chip $AMBA — Edge vision processing $QCOM — Low-power AI decision chips $NXPI — Control + sensor fusion backbone Sensing / Memory (Real-Time Awareness) $ADI — Converts physical signals into data $MU — High-speed memory for instant decisions $OUST — Affordable LiDAR enabling scale Materials (The Constraint) $MP — Rare earth monopoly = motor supply control Simulation (Build Before Reality) $CDNS — Every robot is simulated before deployment Proven Monetization $ISRG — Physical AI already generating recurring revenue Deployment Layer (Real-World Execution) $ONDS — Drone + defense infrastructure $SERV — Autonomous delivery at scale Software / Interface (Control Layer) $PATH — Orchestrating humans + AI + robots $SOUN — Voice + vision interface for machines

@investmentguru_Apr 19, 2026 · 11:42 AM

$ISRG — The Blueprint For How Physical AI Gets Monetized Surgical robotics is Physical AI that already prints recurring revenue. With an installed base exceeding 10,763 systems globally, da Vinci 5 continues gaining momentum with AI-powered force feedback and in-console video replay for real-time surgical decision-making. Every humanoid robotics company is trying to build what ISRG already has. $ONDS — Drones Are Physical AI. And Ondas Is Building the Sovereign Security Grid. Drones are the first large-scale deployment of Physical AI — whoever controls drone swarm technology controls the next generation of warfare and commercial autonomy. Management raised 2026 revenue guidance to $375M — an 840% YoY growth trajectory — driven by Roboteam ground robotics and drone-in-a-box deployments across UAE and Saudi Arabia. Most speculative on this list. Highest upside if execution holds. $PATH — The Orchestration Layer Between Human Workers and Robot Workers Maestro coordinates AI agents, software robots, and human workers across complex enterprise workflows — the central nervous system for autonomous business operations. 90% of U.S. IT executives see agentic potential, 77% plan investments in 2026, but only 37% are live. PATH is where PLTR was in mid-2023. The re-rating hasn’t happened yet. $SOUN — Every Physical AI Machine Needs a Voice. SoundHound Wants to Own That Layer. Robots need to take commands. AVs need to understand context. Machines need ears. At CES 2026, SoundHound unveiled Vision AI for vehicles — uniting visual with voice AI so an in-vehicle assistant can listen, see, and interpret the world simultaneously. Named a leader in the Aragon Research Globe for Agent Platforms 2026. Speculative — but 400+ patents and 217% revenue growth says the moat is real. $SERV — Jensen Huang Called Them Out By Name. The Robots Are Already Delivering. Not a concept. Not a demo. Serve has deployed over 2,000 robots across the U.S. — the nation’s largest sidewalk delivery fleet — completing thousands of deliveries weekly for Shake Shack, Little Caesars, Uber Eats, and DoorDash. The 2026 acquisition of Diligent Robotics expanded Serve into hospitals — Moxi operates in 25+ U.S. hospitals generating $200K–$400K annual revenue per deployment. Nvidia spotlighted. T-Mobile partnered. Real deployments, real data, real moat forming. Stack Map: → Silicon/Compute: $NVDA $ARM $AMBA $QCOM $NXPI → Sensing/Memory: $ADI $MU $OUST → Materials: $MP → Simulation: $CDNS → End Market: $ISRG → Deployment: $ONDS $SERV → Software/Interface: $PATH $SOUN Not financial advice. DYOR.

@investmentguru_Apr 19, 2026 · 11:42 AM

Physical AI Playbook- Wave 1 was digital AI — data centers, GPUs, LLMs. Wave 2 is Physical AI — robots, drones, AVs. $NVDA — The God of Physical AI Every robot OS runs on Nvidia. Jetson Thor delivers 7.5x the performance of its predecessor — already adopted by Amazon Robotics, Boston Dynamics, Figure, and Caterpillar. Simulation, foundation models, edge compute — Nvidia owns the entire stack. You don’t beat the platform. $ARM — The Invisible Tax On Every Robot Ever Built You’ll never see Arm’s logo on a robot. You’ll pay them every time one ships. Arm’s cores sit inside virtually every chip in edge AI — Nvidia’s Jetson, Qualcomm’s Dragonwing, NXP’s S32, Ambarella’s CVflow. As chips get smarter, Arm captures a larger royalty per unit. Pure leverage play on the entire Physical AI buildout. $AMBA — Robots Need Eyes. Ambarella Makes Them. When a robot drops a glass, it can’t wait for a signal to go to the cloud and back. Ambarella makes low-power AI chips that process vision on the edge instantly. Real-time edge vision is a hard engineering problem — Ambarella has the lowest power, highest accuracy solution. No vision, no robot. $QCOM — The Phone Chip Company That’s Actually a Robot Chip Company The market still prices Qualcomm as a smartphone play. Qualcomm’s Dragonwing IQ10 SoC enables low-power decision-making for drones, robots, and AVs beyond the cloud. When Physical AI re-rating hits this ticker, the gap closes fast. $NXPI — The Unsexy Chip That Every Humanoid Robot Will Need Nobody talks about NXP. They should. In March 2026, NXP announced foundational robotics solutions developed with Nvidia — combining Nvidia’s Holoscan Sensor Bridge with NXP SoCs to enable sensor fusion, machine vision, and precision motor control for humanoid form factors. NXP’s processors are purpose-built for the zonal architecture shift — from hundreds of isolated control units to a few centralized superchips. These hit volume production in 2026. Once designed in, stays for a decade. $ADI — The Nervous System No One Is Investing In Every robot needs to feel the world before it can act on it. Analog Devices converts real-world pressure, motion, and temperature signals into data robots can use. ADI stands out for resilient profitability, dividend safety, and growth exposure to robotics. The boring bottleneck with a blue-chip balance sheet. $MU — Speed Is Life When Your Robot Is Walking A robot walking and making a decision can’t afford a 1–2 second delay — a one-second lag means it drops something, crashes, or hurts someone. Fast low-power memory at the edge closes that gap. LPDDR5X and HBM are Micron’s answer. Robotics demand here is structural, not cyclical. $MP — No Rare Earths, No Robot Motors. Full Stop. You can’t build a million robots without the magnetic materials that make their motors spin — and the rare earth supply chain is severely constrained. If the Pentagon wants U.S.-made robots, they need MP’s California mine. The geopolitical moat is the thesis. $OUST — LiDAR Fell 99% in Cost. The Mass Deployment Era Just Started. Ouster is benefiting from a 99%+ cost decline in LiDAR since 2019, helping expand AV and robotics beyond R&D into real-world deployment. Cheap LiDAR is the unlock — robots finally have affordable spatial awareness at scale. $CDNS — You Can’t Ship a Robot You Haven’t Simulated First Cadence’s $3.18B acquisition of Hexagon’s Design & Engineering business brings industry-standard multibody dynamics simulation tools essential for Physical AI and robotics development. Every robot company is a Cadence customer before it ships a single unit. Invisible infrastructure, zero competition.

@jasonl_capitalApr 18, 2026 · 10:33 PM

Electronics & Semiconductors • $QRVO ($82.11) — SATCOM beamformer ICs for phased-array terminals and satellite payloads. Pending Skyworks merger creates a $22B RF platform. • $ADI ($372.48) — Precision analog and beamforming silicon with a dedicated Commercial Space Products program qualified for LEO and GEO missions. • $COHR ($347.47) — Laser-based optical interconnects for satellite-to-satellite and ground comms. Pentagon building laser comms into next-gen space architecture. Nvidia just took a $2B stake.

@stocksavvyshayApr 18, 2026 · 1:29 PM

SPACE ECONOMY CHEAT SHEET A SpaceX IPO likely forces the market to look beyond rockets and start repricing the full supply chain and infrastructure layer beneath the space economy: Spacecraft & Launch Systems • $YSS manufactures standardized satellite buses for rapid deployment • $RKLB builds launch vehicles, spacecraft systems & broader orbital infrastructure • $VOYG supplies space infrastructure & in-space services for government programs • $LUNR provides lunar landers & surface delivery systems tied to NASA Artemis missions • $FLY develops launch vehicles & lunar lander services for commercial & government missions • $RDW manufactures spacecraft components, deployable structures & in-space manufacturing systems Satellite Operators & Services • $BKSY provides real-time geospatial intelligence & high-revisit satellite imagery • $IRDM operates the only truly global LEO satellite network covering the entire planet • $SPIR delivers satellite-based data & analytics across maritime, weather & RF monitoring • $AMZN building a direct-to-device & broadband satellite network through Project Kuiper + $GSAT assets • $PL operates the largest Earth observation constellation serving commercial, defense & intelligence customers • $ASTS building a space-based cellular broadband network for D2D connectivity with BlueBird 7 launching April 19 & 45-60 satellites targeted by year-end 2026 Raw Materials & Mining • $AA supplies aluminum used in launch vehicles & spacecraft structure • $FCX provides copper that feeds power systems & electronics across the space stack • $TECK offers diversified exposure to critical minerals tied to aerospace & industrial buildout • $MP controls the only scaled rare earth mining & processing asset in the U.S. supporting satellite & propulsion supply chains Specialty Materials & Alloys • $ATI produces titanium & nickel alloys critical to propulsion & aerospace systems • $HXL makes carbon fiber composites used in launch vehicles & satellite structures • $PKE manufactures advanced composite materials for aerospace & defense structures • $CRS supplies high-performance alloys used in rocket engines & spacecraft component • $GLW supplies specialty glass & optical components for satellite communications & sensing • $MTRN provides advanced materials & precision components for defense and space systems Electronics & Semiconductors • $QRVO supplies RF chips & filter components for satellite communications payloads • $LITE supplies optical components & lasers used in high-speed communications links • $ADI supplies precision analog & sensing chips used across satellite and launch systems • $COHR provides lasers & optical components tied to satellite & communications systems • $NVDA provides AI compute infrastructure for satellite data processing & autonomous systems • $STM provides semiconductors for guidance, control & communications in space-grade applications • $AVGO supplies connectivity & networking semiconductors used across communications infrastructure

@thevalueistApr 17, 2026 · 5:25 PM

$ADI ascending to an all-time high today. +5% on the day. One more great looking breakout chart. I am long 1/21/28 calls. https://t.co/KSKGpHADrK https://t.co/GUK3RZmnKU

@stocksavvyshayApr 11, 2026 · 3:43 PM

The AI Inference boom is now driving demand across the full compute stack from silicon & memory to optics, power, packaging & cloud: • AI Silicon: $NVDA, $AMD, $AVGO, $INTC • Foundry & Packaging: $TSM, $AMKR, $GFS • Memory & Storage: $MU, $SNDK, $WDC, $STX • Cloud Platforms: $AMZN, $GOOGL, $MSFT, $BABA • Power & Analog: $TXN, $ADI, $NXPI, $STM, $MPWR • Optical Network: $LITE, $COHR, $AAOI, $TSEM, $GLW, $MRVL, $AXTI

@stocksavvyshayMar 30, 2026 · 1:27 PM

Top 20 semiconductor companies by TTM earnings: 1. $NVDA ~$117B 2. $TSM ~$44B 3. SK Hynix ~$31B 4. $AVGO ~$26B 5. Samsung ~$26B 6. $ASML ~$13B 7. $QCOM ~$13B 8. $AMAT ~$6.8B 9. $LRCX ~$6.8B 10. $MU ~$6.6B 11. Tokyo Electron ~$4.6B 12. MediaTek ~$4.4B 13. $AMD ~$4.0B 14. $ADI ~$3.8B 15. $NXPI ~$2.8B 16. Infineon ~$2.6B 17. $KLAC ~$2.4B 18. Renesas Electronics ~$2.1B 19. Advantest ~$1.8B 20. $STM ~$1.3B

@thevalueistMar 10, 2026 · 2:01 AM

$ADI Analog Devices (ADI) Q1 FY2026 Post-Earnings Debrief https://t.co/4VPFZ4ErQX Analog Devices delivered a decisive Q1 FY2026 beat with revenue of $3.16B (+30% YoY, +3% seq) and non-GAAP EPS of $2.46 (+51% YoY), both above guidance. The real story is the Q2 guide: $3.5B revenue at midpoint (+11% seq) with operating margin expanding to 47.5%, representing the strongest sequential acceleration since the Maxim acquisition. CEO Roche explicitly called FY2026 a potential "banner year" — language that is unprecedented for historically measured ADI management. The investment debate has shifted from "is the analog cycle turning" to "how long and how high does this upcycle run" — with the critical nuance that restocking has not yet begun, meaning the most powerful phase of the recovery remains ahead.

@mrmikeinvestingMar 7, 2026 · 11:41 PM

THE ROBOTICS STACK EXPLAINED: 1. $NVDA & $QCOM provide the vision processors, & compute silicon to allow robots to process data, & make real time decisions. 2. $GOOGL, $MSFT & $META supply the foundation models to give robots reasoning, perception, & interaction abilities. 3. $PLTR, $ORCL & $MSFT convert massive robot fleet data into actionable intelligence while $PANW & $CYBR secure the systems. 4. $ARM, $SNPS & $CNDS design the chips powering robot brains. 5. $MBLY & $TDY provide the vision systems, cameras, & perception sensors to allow robots to understand the world. 6. $MP supplies rare earth magnets used in electric motors to move robotic systems. 7. $ADI, $ON & $STM provide the analog semiconductors that connect digital AI systems to motors, sensors, & power electronics. 8. $ST, $RRX & $TKR manufacture the bearings, motors, & mechanical components for robots. 9. $HON & $ROK deliver the industrial automation platforms that integrate robotics into factories. 10. $TSLA, $AAPL, $BABA & $AMZN are building competing robotic ecosystems across manufacturing, logistic, and mobility applications. It’s clear the expansion within the robotics layer is wide, & many names are responsible for this sector boom…

@stocksavvyshayMar 7, 2026 · 1:50 PM

10 WAYS ROBOTICS IS GETTING BUILT OUT ACROSS SECTORS 1. $NVDA is the infrastructure layer for the robotics stack where ever humanoid robot from Tesla Optimus to Boston Dynamics Atlas train in Nvidia’s Isaac Sim virtual environment before ever taking a real step. 2. $TSLA is the only company attempting humanoid robots at manufacturing scale with 1,000+ Optimus units already operating in factories & a long-term ~$25K target price. 3. $GOOGL, $MSFT, $META & $BIDU provide the foundation models that give robots the ability to reason, see & act. 4. $PLTR & $ORCL turn robot fleet sensor data into operational intelligence while $PANW & $CRWD secure the robot “nervous system” as fleets scale & the attack surface grows. 5. $ARM, $SNPS & $CDNS design the chips that power robot brains while $TSM manufactures the silicon & $INTC contributes both design + fabrication capacity for edge compute. 6. $XPEV, $BABA, $AMZN & $AAPL are building competing robotics ecosystems across consumer and logistics applications. 7. $HON & $ROK provide the industrial automation & control systems that integrate robots into existing factory infrastructure at scale. 8. $MP supplies the rare-earth magnets that power motors & actuators throughout the robot supply chain. 9. $ADI, $TXN, $ON & $STM provide the analog semiconductors that bridge the digital brain to the physical world by converting sensor signals, managing power & controlling motors with precision. 10. $QCOM Dragonwing chips handle 5G connectivity & on-device AI for robot coordination while $MBLY & $AMBA provide the vision & compute silicon enabling real-time perception.

@thevalueistFeb 23, 2026 · 12:18 AM

I have admittedly been slow to understand the value proposition of $ADI . I believe it will have very strong 2026 combined with a multiple rerating. Stock has been trading very well this year and it looks like the trend will continue. https://t.co/2elpYxOsYy

@thevalueistFeb 22, 2026 · 3:03 PM

$ADI Analog Devices: Cyclical Re-acceleration, the GAI Edge Shift, and Investment Thesis.

@kaizen_investorFeb 18, 2026 · 12:08 PM

$ADI delivered a textbook beat and raise quarter, justifying the 4% rally. Revenue hit $3.16 billion, driven by a sharp rebound in Industrial and Communications. Crucially, they silenced AI doubters with record Data Center orders, proving they are finally capturing the AI power wave. The real catalyst is the guidance: forecasting a record $3.5 billion for next quarter effectively declares the cyclical downturn over. Management underscored this confidence with an 11% dividend hike. The market is buying the stock because this isn't just a recovery, it's an acceleration into a new growth phase with elite 45% margins.

@stocksavvyshayFeb 9, 2026 · 3:52 PM

AI CHIP ECOSYSTEM Upstream • EDA, IP, & Design Tools | $ARM, $CDNS, $SNPS • Semiconductor Equipment | $ASML, $AMAT, $LRCX, $KLAC, $TER Midstream • Foundry & Advanced Packaging | $TSM, $INTC, $GFS, $AMKR • Compute | $NVDA, $AMD, $AVGO, $MRVL • Interconnect | $CRDO, $ALAB, $COHR, $CIEN, $APH, $LITE • Memory & Storage | $MU, $WDC, $SNDK, $STX • Power & Analog | $TXN, $ADI, $STM, $ON, $MCHP, $MPWR Downstream • OEMs & ODMs | $SMCI, $DELL, $HPE

@leveraged_catFeb 6, 2026 · 1:03 AM

Revisiting the analog semis idea - their relative strength, including the smid caps in the space, has been notable in this general risk-off environment. The relative strength got me to sink my teeth in to what is driving these stocks, and at least in part it is none other than underappreciated accelerating growth due to AI capex. While in a sense it may feel risky to get long these names because AI trades are blowing up left and right, to me they're quite interesting: - They're boring cyclical stocks that few if any momentum/leverage traders even think to long - There are signs that the cyclical elements of the companies are exiting a major trough, see $TXN's latest ER - There is surprising, largely unknown (to tourists) accelerating growth due to AI capex My general thesis on the AI trade / blowup is we are going to go through multiple phases. Phase 1 is now: the market transitions from pricing in the capex bingers (msft, amzn, goog, etc.) as FCF/buyback rich companies to capex heavy cyclicals. Phase 2: a potential lull/goldilocks period (which might not come) where the massive built up speculative leverage in the system has washed out and positioning has reset. This will be the golden era for capex beneficiaries. Phase 3: I expect at some point in the future, depending on market forces, we will enter the "slam on the brakes" phase. Here, the capex bingers are going to have to respond to market screaming at them to stop the capex binge by nuking their stock. When this occurs, what we are seeing now in Phase 1 is going to look like a warmup (because it is). Large swaths of the economy are being swept up in the AI trade (I saw a pitch for steel companies as indirect AI beneficiaries, for instance), and when these new drivers of liquidity via capex slam on the brakes the bullwhip will be awe inspiring. This is the current framework I'm working with - I think we are in Phase 1 now and it will be swift, volatile, and pretty much any crowded trade or long duration asset will get killed while the Phase 1 dynamics are unwound. If I am right and we enter phase 2 (and don't just fast forward to phase 3), then there is going to be an excellent opportunity to make outsize returns on where capex flows are going. -- Of course, there are many other factors at play, and several assumptions I'm making - I am assuming that the AI ROIC of the mags will not be satisfactory to satisfy the market - maybe it will be. If that's the case then there is a very different looking path, where we have to contend with the pressures of rising unemployment due to AI alongside the massive productivity increases of capital. I'm also assuming that the market doesn't front-run phase 3 and put us into a recession via the negative wealth affect - this would also likely kill any cyclical, capex beneficiary, quite soon. However, at least with the current strength in a variety of cyclical sectors, there is reasonable doubt this won't happen. -- tl;dr: analog semis are interesting here because they are largely not crowded and under-appreciated AI capex beneficiaries. To name a few: $TXN $ADI $MCHP $ALGM $MTSI There are other ways to play this, but it's very difficult to find anything that doesn't feel extremely crowded and fit this theme, and we are seeing every day what comes for crowded trades.

@leveraged_catJan 29, 2026 · 9:04 PM

$ADI and $TXN look like the new semis stocks you want to own

@stocksavvyshayJan 25, 2026 · 2:12 PM

THE SEMICONDUCTOR VALUE CHAIN The AI Brain • $NVDA data center & AI accelerators • $META custom inference silicon for AI • $MSFT AI acceleration silicon for Azure • $QCOM mobile, edge & automotive SoCs • $AMD CPUs and GPUs for compute and AI • $AAPL in-house SoCs for consumer devices • $TSLA in-house vehicle compute & FSD chips • $GOOGL custom TPUs for cloud AI workloads • $MRVL data center, storage & networking silicon • $AVGO networking, connectivity & custom ASICs The AI Factory • $UMC mature-node & specialty processes • $INTC internal manufacturing & foundry services • $TSM world’s largest advanced-node manufacturer • $GFS specialty, automotive & defense-focused foundry The AI Testing & Packaging • $TER automated test equipment • $AEHR burn-in & reliability testing • $AMKR outsourced assembly and test The AI Blueprint • $SNPS EDA software & silicon IP • $ARM processor IP & architectures • $CDNS chip design & system verification The AI Equipment • $KLAC process control & metrology • $LRCX etch & wafer fabrication tools • $AMAT deposition & materials engineering • $ASML lithography systems enabling advanced nodes The AI Executors • $MU DRAM & NAND memory • $ADI high-performance analog & signal processing • $STM microcontrollers, power & mixed-signal chips • $NXPI automotive & industrial embedded platforms • $TXN analog & embedded chips for industrial & automotive

@mrmikeinvestingJan 22, 2026 · 3:08 AM

These are the top 6 chip stocks recommended by Bank of America that’ll lead the $1.2T sector: 1. Advanced Micro Devices $AMD 2. Nvidia $NVDA 3. Micron Technology $MU 4. Broadcom $AVGO 5. Lam Research $LRCX 6. Analog Devices $ADI These are some of the lowest risk investments… https://t.co/OxUsaqkbqo

@ftr_investorsJan 20, 2026 · 12:16 PM

Here are the 40 quality business with the most free cash flow 💰💎 (without stock-based compensation) 🍏 1. $AAPL | Apple - $85.9B 🤖 2. $NVDA | Nvidia - $71.3B 💻 3. $MSFT | Microsoft - $65.9B 🔍 4. $GOOGL | Alphabet - $49.9B 🇹🇼 5. $TSM | TSMC - $31.9B 👥 6. $META | Meta - $26.0B 💳 7. $V | Visa - $20.7B 🛜 8. $AVGO | Broadcom - $19.3B 💳 9. $MA | Mastercard - $16.4B 🇨🇳 10. $PDD | PDD Holdings - $14.4B 🔬 11. $ASML | ASML - $10.3B 🚬 12. $PM | Phillip Morris - $9.9B ☁️ 13. $CRM | Salesforce - $9.7B 📺 14. $NFLX | Netflix - $8.7B 💉 15. $LLY | Eli Lilly - $8.6B 🌎 16. $MELI | MercadoLibre - $8.3B 🛒 17. $COST | Costco - $8.1B 🎨 18. $ADBE | Adobe - $8.1B 🏨 19. $BKNG | Booking - $7.7B 🧪 20. $ABT | Abbott - $6.3B 🚚 21. $MCK | McKesson - $6.1B 🏢 22. $SAP | SAP - $5.3B 📊 23. $SPGI | S&P Global - $5.2B ⚙️ 24. $LRCX | Lam Research - $5.2B 📃 25. $INTU | Intuit - $4.4B 🚘 26. $TSLA | Tesla - $4.4B 💲 27. $ADYEN | Adyen - $4.4B 💊 28. $CAH | Cardinal Health - $4.1B 🔧 29. $ADI | Analog Devices - $4.0B 🖥️ 30. $AMD | AMD - $4.0B 🩺 31. $SYK | Stryker - $3.8B 🌐 32. $ANET | Arista Networks - $3.6B 💾 33. $MU | Micron - $3.6B 🕵️‍♂️ 34. $KLA | KLA Corporation - $3.6B 👁️ 35. $EL | EssilorLuxxotica - $3.6B ⛏️ 36. $AEM | Agnico Eagle - $3.6B 🫀 37. $BSX | Boston Scientific - $3.5B 🔌 38. $APH | Amphenol - $3.4B ⚡ 39. $ETN | Eaton - $3.2B 🇸🇬 40. $SE | Sea Limited - $3.2B Source: @KoyfinCharts 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 - EPS & Revenue next 3Y >7% - Net income margin >0% - FCF+ - Altman Z-Score >3 - Marketcap >$10B