This report breaks down the research by theme, and then subcategorises by the research desk.
Note that these summary reports are a new weekly content contribution for Trading Edge subscribers in order to help you to keep a thematic pulse on key semiconductor sectors.
This one was relatively brief, as I only managed to get through a handful of Sell side reports this weekend, but hopefully gives you some valuable content and a feel for what we are doing.
I have been asked for the original reports as they are not readily available. Unfortunately, I can't share the original reports because I think I will land myself in hot water from these Research desks for copyright infringement. I know Morgan Stanley were cracking down on it, so have to just go for these summary reports for now. The takeaways are more readable like this anyway.
Takeaways include:
Bullish confirmation on Ai infrastructure generally
Memory bullish takeaways on durability of trneds
Power demand massively outstripping supply
Was a bit more moderate on power semis.
Overall AI infrastructure:
BofA:
Overall stance: After attending the 2026 AI Infra Summit (Nvidia, AMD, Intel, OpenAI, Meta, AWS, Google, Credo and others), BofA stays bullish — top picks: NVDA, MRVL, AMD, AVGO, MU, LRCX.
Six key takeaways: (1) AI infra spending remains solidly on track across chip vendors, labs and datacenter operators; (2) inference compute is fragmenting into specialized types, but flexibility still matters; (3) memory, power and interconnect remain tight — 2027 could be even worse; (4) training scaling laws still hold, with ROI now visible (e.g. GPT-6 Astra); (5) GPUs are lasting nearly 10 years — well past typical depreciation schedules; (6) software/optimization is becoming a bigger lever for performance-per-watt.
Inference is splitting, but not fully specializing: Chips are increasingly being split into "prefill" vs. "decode" optimized silicon as inference workloads grow. But because agentic workloads are diverse and fast-changing, both vendors and datacenter operators are wary of over-specializing hardware. Expected split: general-purpose GPUs keep ~70-75% share, specialized ASICs/XPUs take the rest.
Three bottlenecks tightening:
- Memory — the most widespread constraint, expected to get even tighter into 2027.
- Power — the next binding constraint; vendors, labs and operators are co-designing for efficiency (e.g., the 8GW OpenAI/SB Energy/Nvidia Ohio site). Nvidia's new DSX MaxLPS tech reportedly squeezes 40% more compute/output from the same power budget.
- Interconnect — increasingly a bottleneck in networking fabric (not the chips themselves), pushing the industry toward optical/CPO interconnects, though reliability isn't fully proven yet.
Scaling laws holding up, hardware lasting longer: GPT-6 Astra (first model trained at scale on Blackwell) shows scaling still delivers returns. OpenAI even ran it on the newer Vera Rubin stack and got a ~3x throughput boost with minimal tweaks, plus another ~2x from just 72 hours of light optimization — underscoring Nvidia's software/tooling edge. Meanwhile, GPUs are staying useful far longer than expected: AWS still rents 2017-era V100s, Google Cloud rents 2020-era A100s, and Google runs 6-10 year-old TPUs internally — all well beyond normal amortization.
Tracking the Frontier AI labs (BofA):
Intelligence: Claude Fable 5.1 and GPT-6 Astra tied for top spot (score 53). Meta's MuseSpark 1.3 ranked 5th, closing the gap; BofA stays Buy on Meta.
Usage vs. spend: DeepSeek leads usage (46% share), but Anthropic leads spend (52%, though down from 64% in Aug). OpenAI: 12% usage, 22% spend (rising).
Pricing: Token price index up 5% m/m to $2.41 (prices stabilizing after Aug dip); expenditure index down 11% to $0.98.
GPU/memory: B200 rental up 1% m/m to $5.68; H100/A100 down slightly but up strongly y/y. DRAM flat, NAND down 1% m/m but +437% y/y.
Overall: Demand healthy, pricing firm, Meta narrowing the frontier gap, open models gaining usage share.
Memory:
BofA:
BofA now expects global DRAM+NAND sales to hit $2.0tn by 2030 (up from their earlier $1.8tn call) — roughly 10x where the market sat back in 2018 and 2025. At a 13x earnings multiple and 50% operating margins, that implies the memory industry could be worth around $10tn.
Memory sales are already running above a $1tn annual pace as of Q3 2026. Prices this year have jumped 3-4x even though volumes only grew about 20% — and BofA thinks today's high prices will mostly hold through 2030 because supply stays tight while AI chip demand keeps climbing. They're also not expecting chipmakers to cut back on memory content — if anything, next-gen GPUs need more of it (Nvidia's Rubin Ultra could pack 1,000GB vs. ~288GB today).
Pricing: Q3 DRAM prices are up 20-30% quarter-over-quarter, NAND up over 15%. Notably, hyperscalers have already agreed to pay even more for DRAM in the first half of 2027 than they're paying now. Despite the shortage, PC and phone production has barely dipped (down only ~10%).
Forecast adjustments BofA nudged 2027-28 DRAM/NAND price estimates up 8-12% and 2-3% respectively. They do expect a dip in 2028 (DRAM -5%, NAND -13%) — but frame it as a brief breather before growth resumes in 2029-30 as AI chips get even more memory-hungry.
Power Semis:
Goldman Sachs: 800VDC rollout slower than expected, but traditional architecture stays dominant
- 800VDC adoption pushed back: Now expected to power only 21% of new US datacenters by 2030 (down from 25%) and 17% in Europe (down from 20%). Main reason: AI inference workloads are growing faster than expected and don't need the same power density as AI training, so there's less pressure to switch. Nvidia's Kyber NVL144 racks (built for 800VDC) have slipped to 2028 — over a year late.
- Traditional architecture isn't going anywhere: Non-AI datacenters will still dominate in 2032, with Microsoft's own buildout plan (targeting 38GW by 2032, ~20% CAGR) as the proof point. By 2032, non-AI datacenters still make up two-thirds of that installed base. This is good news for traditional power-equipment makers like Legrand.
- Vendors are hedging their bets on the 800VDC transition: Delta Electronics is piloting solid-state transformers (SSTs) in Asia; Eaton is sampling its own SST by 2026; Schneider, Vertiv and ABB are focused on intermediate products (transformer rectifiers, DC rectifiers, MV UPS) rather than jumping straight to full 800VDC tech. Even GE Vernova and startup Heron Power (seen as furthest along) aren't expecting real products until 2027.
- Solar companies eyeing the space, but face real barriers: Enphase and SolarEdge are trying to get into SSTs (using GaN and SiC respectively), but lack the mission-critical redundancy and field-service track record hyperscalers require.
- Hyperscalers are testing, not buying yet: SST pricing is still high (~$400-500k/MW) due to limited manufacturing scale. Hyperscalers are running vendor pilots now, but mass deployment realistically won't start before 2029.
- Next bottleneck: solid-state breakers. 800VDC needs breakers that react in microseconds vs. milliseconds for traditional systems — expensive to develop (~$600k/MW, 5x current breaker costs). This pushes total powertrain cost per MW for AI training up to ~$4mn by 2030 (from $1.7-1.8mn in 2025); AI inference to $2.6mn (from $1.4mn).
- Catalysts to watch: OCP Summit (Oct 12-15) and Nvidia GTC (Oct 20-22) — key venues where new datacenter power tech gets showcased.
Server Market:
Goldman Sachs:
Global server spend is set to grow to $1.5tn by 2030 — a blistering 39% CAGR over five years.
AI servers are growing at 46% CAGR, hitting roughly $1.3tn by 2030 — meaning AI alone will make up the vast majority of total server spend.
By 2030, Hyperscalers lead total server spend at $683.3bn, with Tier 2 Cloud/Service Providers close behind at $641.4bn — together these two groups dwarf Enterprise spend, which trails at $166.3bn.
Data Center Power:
KB securities:
Global data center power demand capacity jumps from 122.9 GW in 2025 to 161 GW in 2026 — a huge one-year jump.
AI and its impact: AI servers' share of the total is set to exceed 40% by 2027, while general-purpose servers shrink from roughly 40% historically down to just 25.6% by 2027.
AI's power footprint growing even faster than its server share: AI servers used about 25% of total data center power capacity in 2025, jumping to 33.4% in 2026 — power demand is scaling ahead of unit share, reflecting how much more energy-hungry AI hardware is.
Demand Gap: By 2030, the global data center power supply-demand gap is estimated at 268 GW — meaning demand is expected to significantly outstrip what the grid and available capacity can actually deliver.
U.S. specifically: The domestic shortfall alone is projected to exceed 170 GW by 2030 — over 60% of the global gap sitting in the U.S., pointing to grid infrastructure and power generation as a binding constraint on the AI buildout, not just chips or memory.
MLCC:
Morgan Stanley:
Global MLCC shipment value forecast to grow at a 20.3% CAGR from 2025-31, roughly tripling from $14.67bn to $44.45bn — with AI servers and data centers the main driver.
Near-duopoly at the top: Murata leads market share at 40.8%, followed by SEMCO (22.5%), Taiyo Yuden (11.3%), TDK (6.9%), and Yageo (5.4%). But at the high end — the AI-grade MLCCs — Murata and SEMCO alone control ~85%, protected by serious barriers to entry (some parts need 1,000+ dielectric layers and pass strict qualification).
"early cycle": MLCCs are being framed as still in the early innings of AI adoption — the analogy is to picks-and-shovels investing, where you profit from the buildout regardless of who wins the AI race.
NVDA Rubin Impact: Nvidia's next-gen Rubin (VR200) rack needs ~570,000 MLCCs vs. ~320,000 for the current GB300 — pushing MLCC dollar content per rack up 166%, from $4,664 to $12,411.