Lambert

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Lambert

Lambert

@lam600

USC Business School graduate. USC Football! Go Trojans! I love road cycling. Stock Investing! Stay hungry or you will eventually be hungry--Me.

Los Angeles 参加日 Eylül 2008
1.9K フォロー中708 フォロワー
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VenkatP
VenkatP@VenkatP1359·
“TPUs don’t make money — the bottlenecks around them do.”
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Danny Naz
Danny Naz@ThePupOfWallSt·
Google didn’t just launch new TPUs. They split the AI stack. Training vs inference, separate lanes now. That’s a big deal. More compute → more interconnect → more power → more cooling. Follow the chain: $MRVL $CRDO (data flow) $COHR $LITE (optics) $MPWR $VICR (power) $VRT (cooling) $TSM $AMKR (build it) This isn’t one trade. It’s an ecosystem shift.
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Investing visuals
Investing visuals@InvestingVisual·
If you're looking for exposure to the AI memory and HBM supercycle, the DRAM ETF is worth a look. The top 3 memory gaints are all in there: • $SSNLF (Samsung) - 25% • $HXSCL (SK Hynix) - 25% • $MU (Micron) - 24%
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Investing visuals@InvestingVisual

Three businesses control the entire High Bandwidth Memory (HBM) market: • $HXSCL (SK Hynix, 61%) • $MU (Micron, 21%) • $SSNLF (Samsung, 17%) Do you have a favorite?

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Jason Luongo
Jason Luongo@JasonL_Capital·
$NVDA just told you exactly where to invest your money. They just poured $4 billion dollars into photonics. Here are 10 of the most important photonics companies you need to be aware of: 1. $LITE - Lumentum (Lasers) The laser source that powers photonics interconnects. NVIDIA just invested $2B with a multibillion-dollar purchase commitment for advanced laser components. Building a new 240,000 sq ft InP laser fab in North Carolina. Added to the S&P 500. When NVIDIA writes a $2B check to secure your supply, the market is telling you something.
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Carbon Finance
Carbon Finance@carbonfinancex·
If you invested $10K in Nvidia back in 2010… You’d now have $6.2M. $NVDA
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InvestmentGuru
InvestmentGuru@InvestmentGuru_·
Here’s my 15-stock AI infrastructure watchlist AI INFRASTRUCTURE CORE $MRVL → Custom AI silicon for hyperscalers like Google and AWS → Data center now the core growth engine → Strong XPU positioning in AI compute $CRDO → Critical connectivity layer inside AI clusters → Expanding into silicon photonics and optical transceivers → Direct beneficiary of hyperscaler GPU scaling $ALAB → PCIe/CXL connectivity solving AI server bottlenecks → Key enabler for GPU communication efficiency → Strong execution and AI infrastructure leverage $AAOI → Riding the 800G and 1.6T optical upgrade cycle → Vertical integration gives margin and supply edge → Hyperscaler demand remains strong $MXL → Emerging optical DSP player in AI infrastructure → Pivoted from broadband into data center growth → Early in hyperscaler qualification cycle MEGA-CAP AI COMPOUNDERS $MSFT → Enterprise AI leader via Copilot and Azure → Massive distribution advantage through software ecosystem → AI monetization still in early innings $GOOG → Search funds AI innovation and cloud expansion → Strong custom silicon and AI infrastructure strategy → Multiple growth engines beyond search $AMZN → AWS remains the AI cloud backbone → Aggressive AI infrastructure spending → Retail and ads fuel long-term AI investment SEMICONDUCTOR CYCLE PLAYS $AMD → Leading Nvidia alternative in AI compute → Enterprise traction growing with MI300 → Multiple cycle tailwinds in AI and PCs $MU → HBM memory is essential for AI GPUs → Direct play on AI compute demand → Strong AI-driven memory cycle setup $INTC → Foundry turnaround with strategic US importance → Big upside if execution improves → High risk, high reward setup $ARM → Royalty model across global chip ecosystem → Expanding into AI edge and data center → Benefits from industry-wide chip growth CONNECTIVITY, POWER & INFRA $SIMO → Storage controllers powering AI data growth → NAND cycle recovery adds tailwind → Undervalued storage infrastructure play $NOK → Optical and fiber backbone for data traffic growth → Beneficiary of telecom and hyperscaler upgrades → Defensive infrastructure exposure $BE → On-site energy for power-hungry AI data centers → Solves grid bottleneck challenges → Direct energy infrastructure AI play AI is not one stock. It’s chips, memory, optics, networking, storage, and power. Follow the infrastructure. That’s where the real compounding happens. Not financial advice.
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Trade Whisperer
Trade Whisperer@TradexWhisperer·
This is why I own $TSM. TSMC does not pick sides. It collects from all of them. $MU and $DRAM SK Hynix need it because HBM must be physically bonded to the logic die during the CoWoS process. Memory and packaging are now one supply chain. $ARM needs it because virtually every ARM-designed chip in existence is fabricated at TSMC. The architecture and the foundry are inseparable. Own the IP. Own the fab. Own everything in between. $AMD needs it for SoIC chiplet stacking and high performance compute. AMD was the first SoIC customer and is deepening that relationship every generation. $NVDA needs it for 3nm and CoWoS-L packaging. NVIDIA alone has booked over 70% of all CoWoS-L capacity. The anchor tenant of the entire AI infrastructure build. $AAPL needs it for InFO advanced packaging and 2nm exclusivity. Apple funded TSMC's yield learning curve for every major node transition since 20nm. $AVGO needs it for co-packaged optics and high speed networking ASICs. One of the top CoWoS customers behind only NVIDIA. $QCOM needs it for mobile silicon and every Snapdragon platform on Earth. MediaTek needs it for every mobile, TV, and AI edge chip it produces. One of TSMC's largest and most loyal volume customers globally. Hyperscalers need it for proprietary AI ASICs at massive cloud scale. Google, Amazon, and Meta are now competing directly with NVIDIA for TSMC packaging slots. Every AI winner routes through TSMC. Every single one. It is not a semiconductor company. It is the de facto semiconductor index. A proxy for the entire industry that frees every partner to compete while TSMC collects from all of them. You do not need to pick the winning AI chip. You just need to own the foundry that makes them all. $TSM is not a position. It is a thesis.
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Trade Whisperer@TradexWhisperer

If $NVDA wins, $TSM wins If $AVGO wins, $TSM wins If $MU HBM wins, $TSM wins If $SNDK HBF wins, $TSM wins If $AMD AI chips win, $TSM wins If $QCOM silicon win, $TSM wins If $AAPL silicon win, $TSM wins If hyperscalers ASIC wins, $TSM wins Is that clear enough? TSMC All In

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Beth Kindig
Beth Kindig@Beth_Kindig·
SK Hynix reported a more than 5X increase in net profit in Q1 with an operating margin of 72%, more than 14 points above TSMC’s operating margin of 58%. $TSM $MU $NVDA
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Lambert
Lambert@lam600·
@MarcosMillaYT This is an AI generated video of Jensen Huang. Deep fake.
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Marcos Milla
Marcos Milla@MarcosMillaYT·
NVIDIA $NVDA is a $1,000 stock… “$3 trillion in revenue is possible for NVIDIA in the near-future” - Jensen Huang
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Carbon Finance
Carbon Finance@carbonfinancex·
Daniel Loeb’s Stock Portfolio Visualized:
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Dividend Dad
Dividend Dad@DividendDad1·
The S&P 500 is not as diversified as you think.
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Evan
Evan@StockMKTNewz·
Nvidia $NVDA just posted this: “Introducing NVIDIA Nemotron 3 Nano Omni, an open multimodal model that unifies video, audio, image and text reasoning within a single model. Built for agentic AI workloads”
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StockWhale
StockWhale@thestockwhale·
J.P. Morgan just announced: "we are in dire, absolute NEED for nuclear." If you want to become a potential billionaire, make sure you have these nuclear stocks in your portfolio: 1. Oklo $OKLO 2. NuScale Power $SMR 3. Nano Nuclear Energy $NNE 4. Centrus Energy $LEU 5. ASP Isotopes $ASPI 6. Lightbridge $LTBR All my buy and sell signals in Discord @ stockwhale.vip.
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CryptoGoos
CryptoGoos@cryptogoos·
THIS IS MASSIVE: Nvidia $NVDA CEO Jensen Huang expects revenue to surpass $1,000,000,000,000 by 2027. For context: Nvidia made roughly $130B in its most recent fiscal year. This would be an 8x jump.
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Shay Boloor
Shay Boloor@StockSavvyShay·
$NVDA launched Nemotron 3 Nano Omni which is an open omni-modal AI model built for enterprise agents that can process text, images, audio, video, documents & charts with up to 9x higher throughput than comparable open models. Nvidia clearly moving deeper into the model layer by building faster & cheaper AI models that make its platform more central to how enterprise AI agents actually get deployed.
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Thomas James Investing
Thomas James Investing@Thomas_james_1·
Nvidia CEO Jensen Huang just revealed the ‘Five Layer’ AI Model. These 5 layers include; 1. Energy - $BE $OKLO $VRT 2. Compute - $NBIS $IREN $CIFR 3. Photonics - $AAOI $LITE $SIVE 4. Memory - $MU $SNDK $STX 5. Chips - $NVDA $AMD $AVGO All of these stocks will explode in 2026 & 2027 as AI continued to expand. (This list isn’t exhaustive, just some of my favourites).
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Exec Sum
Exec Sum@exec_sum·
BREAKING: $1 trillion in 'taxable' wealth has left California since the launch of a one-time 5% tax proposal on billionaires Larry Page, Sergey Brin, Zuck, Peter Thiel, Don Hankey, Travis Kalanick, and Andy Fang are among the billionaires packing up
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Sergey
Sergey@SergeyCYW·
From GPUs to Gigawatts: Mapping the AI Data Center Stack 🧵 GPUs create the demand, but the data center stack decides how much compute can actually be used. Memory feeds the accelerators. Networking keeps clusters synchronized. Servers turn silicon into deployable systems. Storage moves data into the pipeline. Power and cooling set the hard physical limits. Compute silicon $NVDA $AMD $INTC $AVGO Memory and Storage SK Hynix, $SNDK $MU Samsung $WDC $PSTG $NTAP Networking & Connectivity $ANET $CSCO $MRVL $CRDO $CIEN Neo Clouds $NBIS $IREN $CRWV 👇
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