Günther

love it, leave it or change it

[Evan @StockMKTNewz]

SK Hynix stock is about to begin trading on the US exchanges on July 10th

[Mark Sterling @MarkSterling0]

President Trump has literally been telling you to buy AI themed names…

These are the 5 layers of AI that you need to be invested in:

1. Energy ~ $CEG, $VST, $OKLO, $EOSE, $GEV

2. Chips & Compute~ $NVDA, $AMD, $TSM, $MU, $ARM

3. Neo-Cloud ~ $NBIS, $IREN, $CRWV, $APLD, $CIFR

4. AI Models - $MSFT, $GOOGL, $META, $AMZN, $ORCL

5. Applications - $AAPL, $TSLA, $NOW, $SNOW, $CRM

The AI themes expansion over the next 6 months will be life changing for many.

Many people ask me: why don’t you charge?
The answer is simple: I’ve already made enough.
Sharing is my passion — that’s why I insist on publishing for free.

[17:17 @17017Nov]

Beautiful🌿💜💜💜#tulips

[Günther @gunwin69]

@firstadopter great

[tae kim @firstadopter]

An NVIDIA executive just told me the GPU-to-CPU ratio will go from 2:1 today to 1:1 “in months" due to agentic AI.

NVIDIA executive is saying 1:1 IN MONTHS.

CPU CPU CPU $NVDA

[Rand Group @randgroup]

The AI supercycle will last 15 years. We're in year 3.

Most investors are still buying Phase 1 names while the real money is already rotating into Phase 3.

I mapped the entire cycle into 4 phases with the tickers that matter at each stage:

The AI supercycle is the biggest investment theme of our generation. Bigger than mobile. Bigger than cloud. A 15 year structural shift that will reshape every sector of the global economy. Hyperscalers just committed $725 billion in capex for 2026, nearly doubling last year. Microsoft, Google, Amazon, and Meta each spending over $100 billion individually.

This is not speculation. I've mapped the entire supercycle into four phases so you know exactly where we are and where the asymmetric opportunities sit.

🔴 Phase 1: Already Ran (2023 to 2025)

The foundation layer is complete. $AMD ran 78% in 2025, $NVDA 39%, and $INTC just posted a blowout Q1 that sent the Philadelphia Semiconductor Index above 10,000 for the first time. Chips still power every phase but the generational entries are gone and risk/reward has compressed.

- $NVDA, $AMD, $ARM, $INTC, $AVGO, $MU, $GLW
- Semiconductors, Memory & Storage,Photonics/Optics
- Foundation complete. Still growing but priced for it.

🟠 Phase 2: Peak Buildout (2025 to 2027)

The phase most investors just woke up to. $CEG acquired Calpine to become the largest U.S. private power producer at 55 GW. $GEV up over 200% in a year. $VRT co engineering cooling for NVIDIA's Rubin architecture. $GLW up 74% YTD on optical fiber demand. Nuclear SMRs are the breakout with $OKLO, $SMR, and $BWXT positioning to power data centers directly. Still upside but the obvious names have moved.

- $CEG, $GEV, $VRT, $VST, $TLN, $ANET, $GLW, $MOD, $EQIX $OKLO, $SMR, $BWXT, $NNE
- Power/Grid, Cooling, Networking, Nuclear/SMR Peak buildout.
- Nuclear SMRs are the sleeper.

🟡 Phase 3: The Positioning Window (2026 to 2028)

Where AI escapes the data center and enters the physical world. Most will be late. Tesla converting Fremont to Optimus production, $25B capex, mass production targeted H2 2026. Rocket Lab posted record $602M revenue with $1.85B backlog. $LUNR up 47% YTD with $943M in contracts. $KTOS Valkyrie drone selected for the Marine Corps. The window to position is open right now.

- $TSLA, $RKLB, $LUNR, $KTOS, $AVAV, $PATH, $ISRG $MP, $FCX, $ALB, $ASTS
- Robotics/Autonomy, Space/Defense/Drones, Rare Earths
- This is where the asymmetric risk/reward lives.

🟢 Phase 4: Final Frontier (2028+)

The endgame. Microsoft capex $190B. Alphabet $190B. Amazon $200B. Meta $145B. Google Cloud backlog past $460B. They're building the rails for AI software dominance and AGI. Quantum still early but $IONQ and D Wave are laying groundwork. The platforms that control the software layer win the entire supercycle.

- $MSFT, $GOOGL, $AMZN, $META, $ORCL, $IONQ
- AI Software Dominance, AGI Infrastructure Decade long thesis.
- Accumulate on weakness.

💊 Key Takeaway

- Phase 2 is confirmed ($725B hyperscaler capex)
- Phase 3 is where the smart money positions nowRobotics, space, defense, nuclear
- SMR are the 2026 to 2028 trades
- Most will rotate into these names 12 months too late

15 year supercycle. Not a trade. Phase 1 ran. Phase 2 is priced. Phase 3 is where you want to be.

[Sergey @SergeyCYW]

AI Semiconductor Winners Beyond Nvidia

AI is no longer a one-stock semiconductor trade.

The market is starting to reward the full stack: edge AI, EDA software, GPUs, custom ASICs, foundries, lithography, analog power, wafer tools, inspection, memory, optical interconnects, CPU IP, and domestic manufacturing.

Intel leads at +129% YTD, showing investors are looking for AI winners beyond Nvidia.

$NVDA — Nvidia
YTD: +14%
Nvidia remains the core AI compute platform through GPUs, CUDA, Hopper, Blackwell, Rubin, and full AI factory systems. The latest bull point is scale: nearly 100 Nvidia-powered AI factories are reportedly in flight, with the average GPU count per factory doubling. Hopper inference performance also improved 400% through software optimization, showing CUDA keeps extracting value after hardware ships.

$AVGO — Broadcom
YTD: +16%
Broadcom sits at the center of custom AI silicon and Ethernet networking. Hyperscalers need ASIC partners to build TPU-like and Trainium-like chips, while GPU clusters need high-speed switching. Broadcom now has six major AI silicon customers, a reported $73B backlog, and visibility toward $100B+ cumulative AI chip revenue by 2027. AI networking is also becoming a larger piece of the mix.

$TSM — Taiwan Semiconductor
YTD: +29%
TSMC is the manufacturing bottleneck behind Nvidia, AMD, Broadcom, Apple, and other advanced AI chip designers. Its 3nm chips now account for 25% of total revenue, up from 6% in late 2023, showing how fast leading-edge demand has shifted. Management is pushing capex above $40B to add 3nm-capable capacity as AI orders strain existing fabs.

$ASML — ASML Holding
YTD: +29%
ASML owns the EUV lithography layer required for sub-5nm AI processors and HBM logic dies. No EUV, no leading-edge AI chips at scale. The key update is memory demand: memory customers accounted for 51% of new machine sales in Q1, up from 30% the prior quarter. Guidance of €36B-€40B for 2026 reflects chipmakers racing to expand AI capacity.

$ADI — Analog Devices
YTD: +41%
Analog Devices benefits from the less visible AI infrastructure layer: power delivery, voltage regulation, precision sensing, and thermal management. Dense GPU clusters need higher-voltage architectures and tighter control. ADI reported record data center product bookings, with its data center division accelerating after 50% growth in the prior fiscal year. AI power complexity is the core thesis.

$LRCX — Lam Research
YTD: +47%
Lam provides etch and deposition tools needed for HBM, advanced packaging, and 300+ layer 3D NAND. AI chips require more process intensity than traditional chips, with up to 30% more etch and deposition steps per unit. A Q2 guide 9.4% above consensus signaled strong demand in AI memory and packaging. Lam wins when chip structures get taller, denser, and harder to build.

$AMAT — Applied Materials
YTD: +48%
Applied Materials supplies materials engineering, hybrid bonding, GAA transistor tools, and advanced packaging systems needed to stack HBM and connect memory to AI logic. Its new Viva platform targets Gate-All-Around transistors, while Sym3 Z Magnum supports advanced etch. The bull case is process complexity: AI chips need more layers, tighter integration, and better packaging.

$KLAC — KLA Corporation
YTD: +49%
KLA sells the inspection and metrology tools fabs need when chip defects become too expensive to tolerate. AI chips, HBM, and advanced packaging raise inspection intensity. KLA’s advanced packaging systems revenue is on track to exceed $925M, up 70%, driven by tight defect thresholds in HBM production. In AI semis, yield is not optional. It is margin protection.

$AMD — Advanced Micro Devices
YTD: +51%
AMD is the main high-volume alternative to Nvidia in data center GPUs. Instinct MI-series accelerators give hyperscalers leverage as they optimize for cost per token, availability, and multi-vendor supply. A key milestone: AMD generated more revenue from GPUs than CPUs in a quarter for the first time. Tier-1 hyperscalers scaling Instinct clusters keep the second-source thesis alive.

$TXN — Texas Instruments
YTD: +53%
Texas Instruments does not need to build AI logic chips to benefit from AI data centers. It supplies foundational analog and embedded chips for power management, server infrastructure, and control systems. Data center revenue reached $1.5B, now 9% of total revenue, and is expanding at a 70% annual rate. Its 300mm expansion could turn AI infrastructure demand into operating leverage.

$MU — Micron
YTD: +77%
Micron is a direct play on the AI memory wall. It supplies HBM for GPUs and PCIe Gen6 enterprise SSDs for massive training datasets. The company has reportedly sold out all 2026 HBM supply under locked pricing agreements. HBM also consumes 3x the wafer capacity of standard memory, tightening DDR5 supply. Data center SSD sales crossed $1B in a single quarter.

$MRVL — Marvell Technology
YTD: +80%
Marvell connects AI accelerators through custom ASICs and electro-optic interconnects. Its custom ASIC business reportedly scaled from near zero to $1.5B annual revenue in fiscal 2026. The $3.25B Celestial AI acquisition adds photonic interconnect technology, strengthening Marvell’s position in high-speed data movement. The bull case is simple: AI clusters need faster pipes.

$ARM — Arm Holdings
YTD: +82%
Arm provides energy-efficient processor architecture used across cloud custom silicon. Its big shift is moving from pure licensing into direct AI server hardware. The Meta-backed Arm AGI CPU is designed for AI inference server scheduling and data-load management. Management has suggested this data center hardware push could reach $15B annual revenue within five years, compared with roughly $4B annual revenue in 2025.

$INTC — Intel
YTD: +129%
Intel’s AI thesis is a turnaround plus foundry optionality. Xeon remains the head-node CPU layer in many AI servers, while Granite Rapids and Gaudi 3 support the data center recovery. Early Gaudi 3 deployments at two major hyperscalers add credibility. The market is also pricing CHIPS Act-backed U.S. foundry potential as hyperscalers look for secure custom AI silicon capacity.

[Patrick @Stock_Bonvivant]

Hierbei aber bitte nicht vergessen, dass Halbleiter zyklisch sind. Meiner Meinung wird sich dies auch nicht ändern, denn die Hyperscaler werden nicht ewig ihre Ausgaben nur für Rechenzentren verwenden. Hinzu kommen die steigenden Verzögerungen bei Bau von Rechenzentren.

[narita flowy @narflowy]

art in cow

[Beth Kindig @Beth_Kindig]

The chatbot boom over the last three years saw GPUs do most of the heavy lifting, yet with agentic workloads – perhaps the single largest catalyst on the horizon for the AI trade in 2026 and beyond – the importance of CPUs is returning.

This week’s newsletter focuses on one company’s move to solve an important bottleneck for agentic AI. Link in bio.

$NVDA $AMD

[Shay Boloor @StockSavvyShay]

YTD performance tells you everything you need to know about CPUs becoming the new AI bottleneck:

• $INTC +110%
• $ARM +93%
• $AMKR +87%
• $AMD +50%

As AI shifts toward inference and agents, the bottleneck moves to the CPU layer that schedules work, manages memory and keeps multi-step systems running at scale.

[Günther @gunwin69]

@FurtureRichKid 350 end of june

[visual arts👩‍🎨 @ArtArchivis_t]

Drawing the letter “S”

[We don't deserve cats 😺 @catsareblessing]

Cutest video I've seen in awhile!!!!!!!

[Epic Clip Vault @EpicClipVault]

Graduation ceremony at a military school in Asia.

[Daniel Romero @HyperTechInvest]

These are all of the AI data-center operator plays available in the market right now:

$CRWV | CoreWeave | MC: $61B
$NBIS | Nebius Group | MC: $26B
$IREN | Iren | MC: $15B
$GLXY | Galaxy Digital | MC: $15B
$APLD | Applied Digital | MC: $9.5B
$CIFR | Cipher Mining | MC: $8B
$RIOT | Riot Platforms | MC: $7.8B
$MARA | Mara Holdings | MC: $7.5B
$CLSK | CleanSpark | MC: $5B
$WULF | TeraWulf | MC: $5B
$HUT | Hut 8 Corp | MC: $4.3B
$BITF | Bitfarms | MC: $2.3B
$HIVE | Hive Digital Technologies | MC: $1.2B
$WYFI | WhiteFiber | MC: $1B
$SLNH | Soluna Holdings | MC: $0.2B
$DGXX | Digi Power X | MC: $0.2B

[Lin @Speculator_io]

𝗧𝗵𝗲 𝗦𝗶𝘅 𝗟𝗮𝘆𝗲𝗿𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗜 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻

Tier 0 - Energy
$LEU $CEG $OKLO $BE $NXT $VST $TLN $GEV $AMSC $NVTS $BWXT

Tier 1 - Chips
$TSM $NVDA $AVGO $AMD $ASML $ARM $INTC $MRVL

Tier 2 - Data Centers
$NBIS $IREN $CRWV $APLD $GLXY $WYFI $ORCL $AMZN $GOOGL

Tier 3 - Foundation Models
$GOOGL $META $NVDA $MSFT $AMZN $BABA

Tier 4 - Software Infrastructure
$INOD $PLTR $BBAI $SNOW $DDOG $MDB $VERI $SYM

Tier 5 - Al Agents
$PATH $CRM $NET $SOUN $PEGA $GOOGL