BofA: Nvidia's $500B Financing > NVDA Underwrites Value, Not Debt: NVDA has signed Memorandums of Understanding (MOUs) with six top financiers (Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR) to mobilize over $500 billion in third-party capital via independent platforms. The financial burden sits with the consortium rather than NVDA's balance sheet. > GPUs as an Investable Asset Class: With $500 billion of capital treating compute as an investable asset class, residual values must hold. NVDA supplies fungible and transferable compute across operators, and CUDA extends its useful life to keep resell/rental rates high and depreciation curves benign. > Extending the AI Buildout Runway: Funding—rather than demand—has been the primary bottleneck. A $500 billion pool allows non-investment-grade buyers (such as labs, neoclouds, and sovereigns) to secure hardware at attractive rates, de-risking offtake and supporting long-term AI systems Total Addressable Market (TAM) paths. > Key Debates and Risks Monitored: Notable cautions remain, including the fact that MOUs are not deployed capital (requiring real end-customers paying real money), potential power and regulatory pushback, input-cost inflation, and the opacity/complexity that new financing structures might introduce to AI buildouts.

GF Securities: Optical > Strong Demand Projections: Total demand for 800G/1.6T is expected to reach 80 million units each in 2027, driven by ramping accelerator demand from Nvidia, Google, AWS, and other ASICs, as well as rising GPU/ASIC scale-out bandwidth and optical module ratios. > CPO/NPO Adoption Timelines: CPO and NPO are being developed side-by-side in next-gen infrastructure, with CPO acting as the long-term design for lower power and latency, while NPO offers better short-term manufacturability and deployment flexibility starting around 2H27. > Nvidia Architecture Shifts: Each Rubin GPU is equipped with two CX9 NIC chips (doubling scale-out bandwidth versus Blackwell), and Rubin Ultra is estimated to adopt four CX9 chips per GPU, shifting the 1.6T optical module ratio from 1:2.5 up to 1:5. > Google's Scale-Up and Scale-Out Transition: Google is expected to fully transition to large-scale clusters in 2026 where the scale-up layer adopts optical interconnects, resulting in an overall TPU-to-optics ratio of approximately 1:4 (1.6T equivalent). > Supply Chain and Component Value Impacts: NPO shifts signal conditioning to system designs, lifting the value of TIAs and laser drivers to tens of dollars per 3.2T optical engine, benefiting supply chain players like Marvell and SMTC. $AAOI $MRVL $COHR $LITE $GOOGL $NVDA

Astera Labs on NPO/CPO: "Once NPO gets deployed and database continues to increase, we will start to see CPO getting deployed. We look at it as 2027 being the year where NPO gets deployed, and then 2028 and beyond is when CPO gets deployed." $AAOI $LITE $TSEM $COHR $NVDA $TSM

Morgan Stanley: Cloud CapEx Cloud Capital Expenditure (Capex) Surges > Massive Uptick in 2027 Forecasts: Following recent US hyperscaler earnings, consensus for 2027 cloud capex has jumped significantly. The forecast is now tracking at approximately $1.2 trillion, a growth of around 30% year-over-year. This is a dramatic increase of about $170 billion and 15 points higher than predictions made just before the recent earnings. > A "too conservative" Consensus? While the current forecast predicts a slowdown in growth (to 29% Y/Y) for 2027 after an incredibly strong 2026, the report suggests this may be an underestimate. Morgan Stanley's own estimate is even higher at $1.4 trillion. The authors also note that aggregate estimates for the top 14 spenders have gone up in each of the last ten quarters. > The AI Impact: The sharp increase in spending, which is ~4x the historical capital spending intensity average for the sector, is clearly fueled by investment in artificial intelligence. The text notes that the current 2027 growth forecast of 29% implies that non-AI cloud capex growth would be just 7%. > Historical Spending Patterns: A historical chart shows the highly cyclical and recently explosive nature of this spending. Growth was modest for years, with negative growth as recently as 2019 and 2023, before skyrocketing to a projected 97% in 2026 and then potentially cooling to 29% in 2027 (though, as mentioned, that 29% figure is up significantly from a forecast of 14% just a month ago). $GOOGL $AMZN $MSFT $META

Breakdown of the memory spend for hyperscalers. UBS projects DDR spend of $190.6 billion in 2026 and then $448.9 billion in 2027, an increase of ~135%. Total memory spend to increase by 127% in 2027. $MU $DRAM $EWY

Morgan Stanley: The Paths to 25-50% GenAI ROIC GenAI ROIC Frameworks & Unit Economics Despite surging AI capital expenditures and model training spend, Morgan Stanley is bullish on long-term ROIC, introducing three bottom-up frameworks that point to attractive 25% to 50% ROIC: > Hyperscaler GPU Rental (IaaS): Estimated to generate ~60–70% incremental EBIT margins and 30%+ ROIC. The base-case analysis assumes deployment on NVIDIA GB300 chips with a 75% utilization rate and a rental price of $8.50/hour. > Model-Enabled API (Owned Infrastructure): Estimated to deliver ~70%+ incremental EBIT margins and 40%+ ROIC. Key success drivers include token pricing, token throughput (tokens/second/GPU), and managing the trade-off of dedicating compute capacity toward training versus revenue-generating inference. > Model-Enabled API (Third-Party Infrastructure): Estimated to yield ~30% incremental EBIT margins and ~25% ROIC, accounting for the "middle-man margin" paid for renting third-party compute capacity. Key Structural Trends in GenAI Adoption > Cost Efficiency vs. Revenue Growth: Morgan Stanley’s global AI stock mapping indicates that roughly 80% of near-term AI benefits stem from cost efficiency rather than immediate top-line revenue growth. AI Adopter EBIT margins expanded significantly, doubling the pace of the broader MSCI World index. > Diverging Earnings Revisions: Since late 2023, forward earnings expectations for global companies successfully adopting AI ("AI Adopters") have outpaced disrupted counterparts by roughly 2x, as concrete productivity gains and margin expansions materialize on balance sheets. > The "Enabler" Divergence: In contrast to general corporate adopters, AI Enablers (such as infrastructure providers and data center chip makers) see a heavy tilt toward revenue growth, with roughly 71% deriving major benefits from top-line expansion driven by high-demand hardware and cloud compute sales. $NVDA $AMD $GOOGL $AVGO $AMZN $META $MSFT

J.P. Morgan: AI Server Market "The AI Server market is estimated to expand to $356bn in 2026 from $195 bn in 2025, implying +83% y/y growth, while the long-term CAGR from 2026 through 2030 is expected to track at +37%. With respect to customer types, Hyperscalers are expected to track to a CAGR of +28% from 2026 through 2030, while Enterprise and Rest of Cloud are expected to drive faster growth at +47% and +49% CAGRs, respectively, over the same period." AI Server Market Growth > Rapid Expansion: The total AI server market is projected to skyrocket from $14.7 billion in 2022 to $1.24 trillion by 2030. > Hyperscale Dominance: Hyperscalers are driving the vast majority of the demand, growing from $8.09 billion in 2022 to an estimated $611.6 billion in 2030. > Massive Peak Growth: The market saw its highest year-over-year percentage growth in 2024 at 211%, with steady, strong growth projected through the rest of the decade. Total Server Market Share > Nvidia's Surge: Nvidia's total market share expanded significantly from 18% in 2023 to 33% in 2025. > Competitor Stability: Major hardware vendors like Dell and Super Micro maintained or moderately increased their standing, with Dell holding 14% and Super Micro at 9% by 2025. AI Server Market Share by Vendor > Nvidia Leadership: Nvidia maintains dominance in dedicated AI servers, commanding 47% of the market in 2025 (down slightly from a peak of 58% in 2024 due to rising competition). > Gaining Competitors: Dell and Super Micro are prominent players in the AI server space, each holding an 11% market share as of 2025.

Nomura Research: TSMC CoWoS TSMC CoWoS Capacity Expansion Trend > Aggressive Upward Revision: According to Nomura estimates, TSMC has turned significantly more aggressive on its CoWoS capacity expansion compared to previous targets (Dec 2025 baseline). > Growth Trajectory: Quarterly capacity is projected to scale up dramatically from around 200 thousand pieces (kpcs) in late 2025/early 2026 to near 600 kpcs per quarter by late 2027. > Divergence from Older Projections: While previous estimates flattened out near 330 kpcs per quarter through 2026 and 2027, current forecasts show continuous sequential expansion starting from mid-2026 onwards. CoWoS Output Breakdown (Volume Growth) > Total Volume Surge: Total output volume is scaling multifold, expanding rapidly from 2023 levels to an estimated peak approaching 2,000 kpcs annually by 2027F. > NVIDIA Dominance in Volume: NVIDIA remains the single largest consumer of TSMC's CoWoS capacity by a wide margin, scaling from a minority share in 2023 to over 1,000 kpcs by 2027F. > Diversification of Hyperscalers: Volume is increasingly supporting customized accelerators and ASICs from major cloud service providers, notably Google and AWS, alongside AMD+Xilinx and Meta. CoWoS Output Allocation Share (%) > NVIDIA Share Stabilization: NVIDIA’s relative share of total CoWoS allocation stabilizes in the 55% to 58% range from 2025F to 2027F, after peaking earlier relative to its initial 2023 baseline. > Google's Expanding Footprint: Google captures the second-largest share of allocation, maintaining a steady slice around 24% to 26% of total capacity through the forecast window. > Other Players: AMD+Xilinx, AWS, Meta, and other networking/FPGA applications split the remaining allocation, with hyperscaler custom silicon taking up a stable overall proportion of advanced packaging lines. $TSM $GOOGL $AMD $NVDA $META $AMZN

Morgan Stanley: Soitec Financial Performance & Estimates > F1Q27 Beat: Revenue came in at €113 million, beating street estimates by 6% and marking a 23% year-over-year growth ( outpacing the company's 15% guide). > Strong Guidance: F2Q27 revenue growth is guided at more than 30% year-over-year, blowing past the 4% consensus expectations. > Significant Earnings Upgrades: Morgan Stanley raised its revenue estimates for FY27–FY29 by 8–19% and upgraded EPS for FY28 and FY29 by 43% and 32% respectively, projecting almost €8 of EPS by FY29. 💡 Core Investment Drivers (Photonics-SOI) > The Growth Engine: The stellar performance and guidance are heavily driven by Photonics-SOI demand for high-speed optical interconnects in AI data centers. > Doubling Revenue: Management explicitly noted that Photonics-SOI FY27 revenue is expected to more than double, acting as the catalyst the market had been anticipating. > Secular Alignment: Soitec’s stellar outlook mirrors broader industry tailwinds from major players scaling up silicon photonics (SiPho) capacity—such as Tower Semiconductor, STMicroelectronics, and TSMC. Photonics is expected to scale from roughly 8% of Soitec's revenue in FY25 to about one-third in the current fiscal year. Scenario Analysis (Price Target Cases) > Bull Case (€300.00): Assumes a stronger recovery in RF-SOI alongside more aggressive, accelerated growth in Photonics-SOI, paving the way for €10 in earnings power. > Base Case (€200.00): Assumes multi-year growth in Photonics-SOI and normalization in RF-SOI, applying a 25x multiple to CY28 (FY29) EPS. > Bear Case (€80.00): Assumes a slower RF-SOI recovery and weaker Photonics growth, applying a 20x multiple to discounted bear-case earnings.

Andrew Bell, Nvidia's $NVDA senior vice president of hardware engineering, said that Nvidia will be able to produce up to 1,000 Vera Rubin racks per day. If so, Nvidia and its manufacturing partners would generate at least $630 billion in revenue in a quarter.

Cantor Fitzgerald: Intel & AMD Intel ($INTC) > Outlook: Cantor maintains a "Neutral" rating with a $150 price target, noting that the risk/reward profile into earnings is skewed to the upside. > Drivers: The company is expected to see a "modest beat/raise" in its upcoming results, fueled by demand for agentic AI and expansion in average selling prices (ASP) within its Client segment. > Key Focus: Analysts are closely monitoring Intel's ability to outperform in a supply-constrained environment and how a potential decline in Client unit sell-through during the second half of the year will impact the company. > Foundry Progress: There is optimism regarding progress on 18A-P and 14A nodes, alongside developments with both current and new customers. This is expected to lead to a forecast for significantly higher capital expenditure (Capex) in 2027. AMD ($AMD) > Upcoming Event: AMD will host its third annual Advancing AI 2026 Event on July 22–23 in San Francisco. > New Tech: The company is expected to launch its next-generation Instinct MI450-series GPUs and Zen 6-based EPYC "Venice" processors. > Infrastructure Growth: AMD is anticipated to provide updates on the third-quarter ramp of its "Helios" rack-scale platform and offer more context on a "rapidly expanding pipeline" of large-scale AI infrastructure opportunities. > Market Projections: AMD is expected to raise its view on the Server CPU Total Addressable Market (TAM) for 2030 to over $150B (up from $120B). There is anticipation for an updated view on the AI Accelerator TAM, with potential upside to $600B–$800B in the 2029/2030 timeframe. > Financial Outlook: Cantor believes AMD is positioned to guide for data center revenues to at least double in 2027. AMD remains a "Top Pick" with an "Overweight" rating and a $700 price target.

Mizuho Securities: CPUs & GPUs Market Forecasts & Growth > Shipment Growth: Industry server CPU shipments are forecasted to reach 35 million units in 2026 and grow to 50 million units by 2027, representing a 40% year-over-year increase. > Long-Term TAM: The long-term Total Addressable Market (TAM) estimate for 2030 has been raised to $170 billion (up from the previous $107 billion forecast), driven by higher CPU-to-GPU ratio assumptions for AI inference servers. > CPU-to-GPU Ratios: The CPU-to-GPU ratio on AI servers is accelerating and is expected to approach 1:1 by the end of 2027 or 2028. Supply Chain & Technical Bottlenecks > DRAM Constraints: A critical bottleneck exists in DDR5/LPDDR5 supply, with a projected fulfillment ratio of only 70% over the next 12–18 months. > Demand vs. Supply Gap: Based on current models, the 2027 demand for DDR5/LPDDR5X (over 300 billion 1Gb equivalents) significantly exceeds the projected supply (220–250 billion 1Gb equivalents). > Potential Risks: The shortage of key materials—DRAM, substrates, and passives—is expected to persist through 2027 and could pose downside risks to downstream server assemblers, potentially leading to lower server rack output. Key Player Insights (2027 Forecasts) > Nvidia: Expected to reach 5.0–6.0 million units for the Vera CPU, including 2.0–3.0 million units specifically for agentic AI stack racks. > Google: Axion CPU production is projected to increase more than 2x year-over-year, aligning with the growth trajectory of TPU units. > AMD: The N2 Venice CPU is forecasted to exceed 6.0 million units. GPUs/ASICs Market Growth Projections > Rapid Expansion: The total AI ASIC market is projected to grow from 4.1 million units in 2025 to 24.0 million units by 2028. > Volume Drivers: The growth is driven by substantial increases in deployment by major hyperscalers including Google, Amazon (Annapurna), Meta, Microsoft, and OpenAI. > External Demand: The market for external (non-Google) AI ASIC units is expected to surge from 0.6 million in 2025 to 7.2 million by 2028. Key Hyperscaler Activity > Google (TPU): Continues to be a dominant player, with total shipment units increasing from 2.5 million in 2025 to 7.1 million by 2028. > Anthropic: Significant ramp-up is forecasted for their "TPU Ironwood/Sunfish" chips, moving from 0.6 million units in 2026 to 6.2 million units by 2028. > Amazon/Annapurna: Shipments for the Trainium line are projected to double from 1.5 million in 2025 to 3.6 million by 2028. > Meta: Rapid scaling of MTIA chips is expected, growing from 0.1 million units in 2025 to 2.7 million units by 2028. Technical Trends > Advanced Packaging & Nodes: There is a heavy reliance on sophisticated packaging technologies like CoWoS-L and CoWoS-S, and advanced foundry nodes including N2, N3, N4, N5, and A16. > HBM Integration: Nearly all listed high-performance ASICs utilize High Bandwidth Memory (HBM), with a transition toward newer generations such as HBM3E and HBM4/4E to meet performance demands. > ASP Variance: Average Selling Prices (ASP) range significantly, from approximately $2,000 for entry-level models to as high as $40,000 for top-tier specialized chips like the TPUv10. $DRAM $EWY $MU $GOOGL $AMKR $TSM $ASE $NVDA $AMD $AVGO $MRVL $INTC $MSFT $META

Goldman Sachs: China AI Compute > China’s national computing power network is part of a "six major networks" infrastructure project projected to attract Rmb7tn investments in 2026. Bloomberg reports data center investments will reach approximately Rmb2tn ($300bn) over the next 5 years. > Capital and technology are heavily shifting toward Western China hubs. Meanwhile, tier-1 city data centers are transitioning to focus on ultra-low latency hot compute, edge nodes, and AI inference. > Operational gigawatt (GW)-scale clusters with 100k+ chips remain scarce in China compared to the US. However, Range Intelligence successfully operationalized a 200MW, 100k chip-scale building year-to-date (YTD) as part of a larger GW-scale cluster layout. > A typical GW-level campus workload profile consists of 50%+ Inference, 20–30% Training, and 10–20% Full-stack R&D. > Domestic AI accelerator chip shipments are expected to exceed 50% market share in 2026, with Huawei (20%) and Alibaba's T-Head (7%) leading the domestic segment (though Nvidia still commands 55% overall). > Capital expenditure per IT power for domestic chips is 40–50% lower than imported chips. However, their performance lags significantly: capex per computing power is 2–4x higher, and computing power per IT power is only 10–30% of what imported chips achieve. > Huawei's 910B/910C servers produce an average daily token output volume that is just $1/6$ to $1/3$ of an Nvidia H800 server. Consequently, API profit margins based on Huawei 910B hardware heavily lag behind Nvidia counterparts.

Goldman Sachs: TSMC $TSM > N3 (3nm) Capacity: Revised upward to 200kwpm by end-2027E (vs. 190kwpm prior) as N3 becomes the primary bottleneck for AI GPUs and ASICs following node migrations. > N2 (2nm) Capacity: Revised upward to 140kwpm by end-2027E (vs. 130kwpm prior). N2 initial-year output is expected to exceed N3's first year by 45%. > Advanced Packaging (CoWoS): 2027E CoWoS capacity has been raised to 280kwpm quarterly (vs. 250kwpm prior). Total annual CoWoS capacity for 2027E is adjusted up to 2,730k wafers (vs. 2,490k prior). > Capex Step-up: Raised 2027E capex to US$78bn (from US$70bn) and 2028E capex to US$82bn (from US$74bn) to secure the AI buildout. 2026E capex remains unchanged at US$56bn. > Gross Margin (GM) Upside: Now modeled to hit 66.9% / 66.8% / 67.3% from 2026 to 2028 (up from 64.9% / 64.7% / 66.5% prior). Better product mix and pricing power are expected to easily offset any dilution from overseas fabrication facilities. $NVDA $AMD $GOOGL $AMZN $META $MSFT $INTC $AVGO $MRVL

Intel $INTC EMIB Supply Chain Flip-Chip Assembly Bumping Powertech Technology $6239.TW Amkor Technology $AMKR Die Bond ASMPT $0522.HK Kulicke & Soffa $KLIC Laser Marking E&R Engineering $8027.TWO Plasma Cleaning E&R Engineering $8027.TWO EMIB Substrate IC Substrate Ibiden $4062.T Unimicron $3037.TWO AT&S $ATS.VI Shinko ABF Film Lamination Ajinomoto $2802.T Eternal Precision Mechanics $7795.TWO Bridge Die Bond Toray $3402.T Electroplating ASMPT NEXX Laser via Drilling Mitsubishi Electric $6503.T Baking Oven Group Up $6664.TWO Other Components Silicon Capacitor AP Memory $6531.TW Samsung Electro-Mechanics $009150.KS Silicon Capacitor Foundry Powerchip $6770.TW United Microelectronics $UMC Winbond $2344.TW Information derived from Nomura Securities, but I included couple names in the supply chain I believe they missed - AT&S, Ajinomoto

iM Securities: Is TSMC Bottlenecking Nvidia's Short-Term Growth? > CoWoS Wafer Revisions: Due to slower-than-expected capacity expansion by TSMC $TSM and other CoWoS suppliers, iM Securities lowered its CY26 global CoWoS allocation forecast for AI accelerators from 1,380K to 1,096K wafers. Consequently, Nvidia's projected AI GPU production for the year has been cut from 11.14 million units (YoY +57%) to 9.24 million units (YoY +31%). > Rubin Delays: Most notably, production projections for the next-generation Rubin GPU have been slashed in half, from 3 million units down to 1.5 million units. > HBM Market Impact: Lower accelerator production drops total CY26 HBM demand from 4.89 billion GB to 4.23 billion GB. Memory manufacturers have adjusted their production down slightly to 4.33 billion GB, factoring in lower-than-expected demand and lower margins compared to conventional DRAM. > Nvidia’s upcoming next-generation AI accelerator, the Rubin Ultra (slated for CY27), is facing technical hurdles that may force a significant specification downgrade. While still under negotiation between Nvidia and memory vendors, a 384GB scaled-down version is being considered instead of the original 1TB (1,024GB) target. > CoWoS-L Size Limitations: The physical limit of the CoWoS-L interposer is 8,150 mm^2. The original 4-die plan requires 6,750mm^2. While mathematically possible, scaling the interposer up dramatically increases substrate warpage, concentrates stress on the corners, degrades bump fatigue life, and tanks packaging yields. Returning to a 2-die architecture automatically drops the maximum HBM cubes from 16 to 8. > TSMC's CoPoS Alternative is Too Late: TSMC’s next-gen solution to bypass this size limit is CoPoS (Chip on Panel on Substrate). However, because TSMC is only just beginning to select equipment and component vendors, mass production is not expected until the second half of 2028 (2H28). This leaves a packaging bottleneck that will stress Nvidia's growth through next year. > HBM4E Stacking Yields: Memory manufacturers are also struggling with the production yields of stacking 16-layer HBM4E, though this is flagged as a memory vendor issue rather than a TSMC-inflicted constraint. > Total AI Accelerator chip volume for CY26 is projected to hit 17,865K (17.87M) units, reflecting a 51% YoY growth. > Nvidia $NVDA commands 56% of the total CoWoS capacity allocation (640K wafers), yielding 9,242K chips (+31% YoY). > Broadcom $AVGO captures 272K CoWoS wafers (a massive 206% increase), resulting in 5,354K chips (+70% YoY). This is heavily anchored by Google's TPU, which takes 75% of Broadcom's share (4,027K chips). Meta's MTIA accounts for 1,017K chips. > AMD $AMD AI GPUs take up 70K wafers, translating to 875K chips (+22% YoY), split between MI350X (560K) and MI400X (315K). > Global HBM demand for CY26 is expected to reach 4,234 million GB (4.23B GB), marking an explosive 95% YoY growth. > Nvidia alone consumes 2,427M GB (roughly 57% of total market demand). > Broadcom represents 1,162M GB of HBM demand (+115% YoY), dominated heavily by the TPU at 851M GB. > AMD accounts for 282M GB of demand, with the upcoming MI400X utilizing ultra-dense HBM4 configurations (384GB per accelerator using 48GB cubes).

Edgewater: Analog Semiconductors > The industry is moving from general supply tightness into early shortages. Demand has accelerated through May and June, with Q2 book-to-bill (B2B) ratios tracking at 1.4x to 1.6x > Lead times are extending significantly. Power and discretes are approaching 52 weeks, while broadline semiconductors are trending toward 30+ weeks. > T-glass has emerged as a major bottleneck affecting substrate and PCB suppliers. Because limited glass supply is being prioritized for AI chips, other semiconductor sockets are heavily constrained. > Companies like $TXN, $IFNNY, and $STM are leading a second round of broad pricing increases (seeking 10–25% hikes) effective July 1st. > Discussions of a potential third round of pricing actions are already beginning to emerge for 4Q26. Additionally, MCHP is expected to implement selective pricing actions in CY3Q due to inflationary pressures. Texas Instruments: $TXN (ST Positive / LT Positive): Facing a supply crunch due to back-end bottlenecks, but pricing actions and supply tightness point to near-term revenue and margin upside. Monolithic Power Systems: $MPWR (ST Positive / LT Positive): Demand continues to outpace supply due to strong AI and CPU programs, positioning the company for near-term revenue upside and gross margin expansion. Vishay Intertechnology: $VSH (ST Positive / LT Positive): Shifting focus toward a margin-accretive business mix with potential upside from strategic capacity investments made during the previous downturn.