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🚨 $NVDA SIGNED A MASSIVE $500B PARTNERSHIP WITH 6 FINANCIAL GIANTS TO ACCELERATE AI BUILDOUT THIS IS ESSENTIALLY TELLING YOU WHERE TO PUT YOUR MONEY 💰👇 HYPERSCALERS— $AMZN $GOOG $MSFT NEOCLOUDS— $CRWV $NBIS $IREN AI CHIPS— $NVDA $AMD $AVGO MEMORY— $MU $SNDK $SKHY NETWORKING— $ANET $MRVL $LITE ELECTRIFICATION— $ETN...

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Leif | Investing

16,106 subscribers

45,973 Aufrufe • vor 1 Monat •via X (Twitter)

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17 Kommentare

Profilbild von Kastriot
Kastriotvor 1 Monat

yes, full invested in $DGXX PT 50$

Profilbild von The AI Therapist
The AI Therapistvor 1 Monat

$500B for hyperscalers. CoreWeave at 25x EV/EBITDA isn't a stock, it's a toll booth on the only road to AGI. buy or die.

Profilbild von Alex DeWolf
Alex DeWolfvor 1 Monat

following the chips is smart but the real bottleneck is grid power

Profilbild von Leif | Investing
Leif | Investingvor 1 Monat

$PWR 📈

Profilbild von Divided we stand
Divided we standvor 1 Monat

Also apld

Profilbild von TOM
TOMvor 1 Monat

No

Profilbild von Adel Bucetta
Adel Bucettavor 1 Monat

the honest answer is that most investors would love to tell you where to put your money, but this partnership doesn't change the fundamentals of nvidia's business. their stock price will likely reflect the actual value created by these partnerships, not just the headlines.

Profilbild von Leif | Investing
Leif | Investingvor 1 Monat

Well said. This partnership brings further visibility to $NVDA future cash flows.

Profilbild von Dray
Drayvor 1 Monat

Wow Lfg

Profilbild von Brendan Gilbert 🚀📈📉
Brendan Gilbert 🚀📈📉vor 1 Monat

The AI trade is expanding far beyond $NVDA. The next phase depends on data centers, chips, memory, networking and power. That makes the AI infrastructure chain just as important as the headline GPU winners. For crypto, watch where capital and liquidity rotate next. Stay ahead of the markets with expert trading guidance: $NVDA $BTC $ETH #AI #Crypto

Profilbild von Crypto Cowboy
Crypto Cowboyvor 1 Monat

Massive

Profilbild von Leif | Investing
Leif | Investingvor 1 Monat

I think AI trade will have a day tomorrow. Keep 👀

Profilbild von Jesse Bean
Jesse Beanvor 1 Monat

Great info

Profilbild von insightsofafarmer
insightsofafarmervor 1 Monat

Don’t forget $STRL

Profilbild von AG3
AG3vor 1 Monat

You may want to mention SPCX in your list. Just saying

Profilbild von C Moody
C Moodyvor 1 Monat

@Congressgohome Wall Street always has the new plan to steal your money...

Profilbild von Brendan Gilbert 🚀📈📉
Brendan Gilbert 🚀📈📉vor 1 Monat

The AI trade is expanding far beyond $NVDA. The next phase depends on data centers, chips, memory, networking and power. That makes the AI infrastructure chain just as important as the headline GPU winners. For crypto, watch where capital and liquidity rotate next. Stay ahead of the markets with expert trading guidance: $NVDA $BTC $ETH #AI #Crypto

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HOW AI IS ACTUALLY BEING BUILT (Cheat Sheet) Jenson Huang CEO of NVDA just walked through the stack in this order. These are the companies to know for each layer. ENERGY Nuclear / Baseload • $CEG • $BWXT • $SMR • $OKLO • $LEU • $UUUU Gas / Power Supply • $TLN • $VST • $NRG • $ET CHIPS GPUs / Accelerators • $NVDA • $AMD Networking / Custom Silicon • $AVGO • $MRVL Memory / Storage • $MU • $WDC • $STX Architecture / Tools • $ARM • $ASML • $LRCX • $KLAC INFRASTRUCTURE Data Centers / Real Assets • $EQIX • $DLR • $IRM Power, Cooling & Electrical • $VRT • $ETN • $HUBB • $PWR HPC / AI-First Infrastructure • $NBIS • $APLD • $IREN • $RIOT • $CIFR MODELS Foundation / Platform Models • $MSFT • $GOOGL • $META Cloud Distribution • $AMZN • $ORCL APPLICATIONS Enterprise / SaaS • $CRM • $NOW • $SNOW • $DDOG • $MDB Creative / Productivity • $ADBE • $INTU Defense / Automation • $PLTR • $KTOS • $AVAV Robotics / Physical AI • $TSLA • $SYM • $SERV • $RR
1:10

HOW AI IS ACTUALLY BEING BUILT (Cheat Sheet) Jenson Huang CEO of NVDA just walked through the stack in this order. These are the companies to know for each layer. ENERGY Nuclear / Baseload • $CEG • $BWXT • $SMR • $OKLO • $LEU • $UUUU Gas / Power Supply • $TLN • $VST • $NRG • $ET CHIPS GPUs / Accelerators • $NVDA • $AMD Networking / Custom Silicon • $AVGO • $MRVL Memory / Storage • $MU • $WDC • $STX Architecture / Tools • $ARM • $ASML • $LRCX • $KLAC INFRASTRUCTURE Data Centers / Real Assets • $EQIX • $DLR • $IRM Power, Cooling & Electrical • $VRT • $ETN • $HUBB • $PWR HPC / AI-First Infrastructure • $NBIS • $APLD • $IREN • $RIOT • $CIFR MODELS Foundation / Platform Models • $MSFT • $GOOGL • $META Cloud Distribution • $AMZN • $ORCL APPLICATIONS Enterprise / SaaS • $CRM • $NOW • $SNOW • $DDOG • $MDB Creative / Productivity • $ADBE • $INTU Defense / Automation • $PLTR • $KTOS • $AVAV Robotics / Physical AI • $TSLA • $SYM • $SERV • $RR

Justin Banks

31,507 Aufrufe • vor 7 Monaten

How to Weight Semiconductors 1) GPUs $NVDA $AMD 2) CPUs $AMD $INTC $NVDA 3) TPUs $AVGO 4) Foundries $TSM $INTC 5) RAM Memory $MU 6) NAND Flash Memory Chips $MU 7) SSDs Flash Memory Finished Drives $MU $SNDK 8) HDDs (Slower long term storage) $WDC All these semi sub sectors are critical to the AI buildout. I think it makes sense to have exposure to all of them. The first question is what percent of your portfolio do you want to be semis. The second is how do you split up that weight amongst these companies. By market cap? Or do you overweight certain names like $AMD if you are bullish on CPUs. Do you utilize a active ETF that does this for you and with tax efficiency to improve returns? These are important questions to ask yourself. Marcos Milla and I Discuss.
3:57

How to Weight Semiconductors 1) GPUs $NVDA $AMD 2) CPUs $AMD $INTC $NVDA 3) TPUs $AVGO 4) Foundries $TSM $INTC 5) RAM Memory $MU 6) NAND Flash Memory Chips $MU 7) SSDs Flash Memory Finished Drives $MU $SNDK 8) HDDs (Slower long term storage) $WDC All these semi sub sectors are critical to the AI buildout. I think it makes sense to have exposure to all of them. The first question is what percent of your portfolio do you want to be semis. The second is how do you split up that weight amongst these companies. By market cap? Or do you overweight certain names like $AMD if you are bullish on CPUs. Do you utilize a active ETF that does this for you and with tax efficiency to improve returns? These are important questions to ask yourself. Marcos Milla and I Discuss.

John Tinsman

47,139 Aufrufe • vor 4 Monaten

You've listened to me. Now you will be rewarded. Here are the opportunities to buy: 1. Software-related stocks $IGV - $MSFT, $CRM, $NOW, $MDB, $ZETA, $DDOG, $ADBE, $ORCL, and many others Emerging: 1. Military-defense stocks - $LMT, $GD, $RTX, $PLTR, $BA, and others 2. Healthcare - $ABBV, $JNJ, $LLY Dip buying opportunities to buy soon (probe positions at good levels): 1. Memory - $MU, $SNDK 2. Mag 7's and honorable mentions - $AMZN, $GOOGL, $MSFT, $TSLA, $NVDA, $AAPL, $AMD, $INTC, and others 3. AI - related - $IREN, $AVGO, $QCOM, $INTC, $ARM, $NXPI, $NOK, $BB Make sure you follow my every move. Every day the market is telling you a story. You need to understand this so you get ahead of everyone else.
5:36

You've listened to me. Now you will be rewarded. Here are the opportunities to buy: 1. Software-related stocks $IGV - $MSFT, $CRM, $NOW, $MDB, $ZETA, $DDOG, $ADBE, $ORCL, and many others Emerging: 1. Military-defense stocks - $LMT, $GD, $RTX, $PLTR, $BA, and others 2. Healthcare - $ABBV, $JNJ, $LLY Dip buying opportunities to buy soon (probe positions at good levels): 1. Memory - $MU, $SNDK 2. Mag 7's and honorable mentions - $AMZN, $GOOGL, $MSFT, $TSLA, $NVDA, $AAPL, $AMD, $INTC, and others 3. AI - related - $IREN, $AVGO, $QCOM, $INTC, $ARM, $NXPI, $NOK, $BB Make sure you follow my every move. Every day the market is telling you a story. You need to understand this so you get ahead of everyone else.

AskLivermore

274,452 Aufrufe • vor 3 Monaten

$NBIS and $CRWV growing like crazy shouldn't be surprising. Satya Nadella publicly explained 6 months ago on a podcast that $MSFT would be leasing a lot of capacity going forward rather than building all itself. Here is what I think is happening: These neo-cloud businesses, both $NBIS and $CRWV, exist solely because $NVDA wants them to exist. Nvidia has an equity stake in both companies, and they are both members of the Nvidia Partners Program. Nvidia grants early access to its chips to the partners. Nvidia does that because it knows all hyperscalers, especially $MSFT and $AMZN, are racing to develop their own cutting-edge GPUs, as this is their biggest cost in scaling cloud operations. Nvidia knows that if they are allowed to control the supply, it will prolong the upgrade cycles and reduce orders to gain time to develop its own chips. This is why it's providing early access to the neo-clouds that are 100% dependent on Nvidia chips. Hyperscalers know that if they delay orders for the fresh capacity, the demand will shift to the neo-clouds that can build capacity fast with the support of $NVDA. Thus, $NVDA corners hyperscalers and strategically compels them to put new orders and cripples their ability to focus on their own chips. $MSFT sees this and says, "If this is the case, I'll just lease the capacity from the neo-clouds." It's basically a strategic response to $NVDA. It leases the bulk of their capacity, making them dependent on it. So, if $MSFT backs down, they'll have a really hard time staying alive. There is a small window of opportunity here for neo-clouds. If they can scale beyond hyperscalers and form a fragmented customer base, they may keep thriving even if hyperscalers reduce leases. Currently, $NBIS has a better shot at it than $CRWV, as $MSFT already makes up 72% of its revenue. I am long $NBIS, and I have already made 4x of money in the stock. I'll remain long $NBIS and even grow my position, but I'll remain skeptical of $CRWV for its unfavorable strategic position, as I explained above.
1:30

$NBIS and $CRWV growing like crazy shouldn't be surprising. Satya Nadella publicly explained 6 months ago on a podcast that $MSFT would be leasing a lot of capacity going forward rather than building all itself. Here is what I think is happening: These neo-cloud businesses, both $NBIS and $CRWV, exist solely because $NVDA wants them to exist. Nvidia has an equity stake in both companies, and they are both members of the Nvidia Partners Program. Nvidia grants early access to its chips to the partners. Nvidia does that because it knows all hyperscalers, especially $MSFT and $AMZN, are racing to develop their own cutting-edge GPUs, as this is their biggest cost in scaling cloud operations. Nvidia knows that if they are allowed to control the supply, it will prolong the upgrade cycles and reduce orders to gain time to develop its own chips. This is why it's providing early access to the neo-clouds that are 100% dependent on Nvidia chips. Hyperscalers know that if they delay orders for the fresh capacity, the demand will shift to the neo-clouds that can build capacity fast with the support of $NVDA. Thus, $NVDA corners hyperscalers and strategically compels them to put new orders and cripples their ability to focus on their own chips. $MSFT sees this and says, "If this is the case, I'll just lease the capacity from the neo-clouds." It's basically a strategic response to $NVDA. It leases the bulk of their capacity, making them dependent on it. So, if $MSFT backs down, they'll have a really hard time staying alive. There is a small window of opportunity here for neo-clouds. If they can scale beyond hyperscalers and form a fragmented customer base, they may keep thriving even if hyperscalers reduce leases. Currently, $NBIS has a better shot at it than $CRWV, as $MSFT already makes up 72% of its revenue. I am long $NBIS, and I have already made 4x of money in the stock. I'll remain long $NBIS and even grow my position, but I'll remain skeptical of $CRWV for its unfavorable strategic position, as I explained above.

Oguz Erkan

180,198 Aufrufe • vor 1 Jahr

THE 5 BIGGEST BOTTLENECKS POWERING THE AI ECONOMY The way I'm thinking about AI winners today is that the market is moving beyond the simple question of which mega-cap company spends most on AI and has been rewarding the companies that control the scarce inputs, contracted capacity, data movement, power infrastructure, edge compute and workflow layers that make the AI economy function. These are the five bottlenecks I'm watching most: • Memory | $MU, Samsung, SK Hynix Memory is the clearest scarce input because HBM feeds the accelerator, only a few companies can make it at volume and buyers are locking in supply through long-term agreements that create revenue visibility through the end of the decade. • Connectivity | $AVGO, $MRVL, $ALAB, $CRDO, $AAOI, $ANET Connectivity determines whether AI clusters can move data fast enough because training runs span tens of thousands of chips that need to act like one machine. Once copper runs out of reach that causes the cluster depends on optics, retimers, switches and custom silicon to keep the system moving. • Power | $CEG, $VST, $GEV, $FPS, $VRT, $NVTS, $TLN, $ON Power determines whether new AI data centers can actually come online because the binding constraint is shifting from getting chips to getting megawatts so the value flows to the companies that control generation, grid equipment, power delivery, thermal management and efficiency. • Compute | $NBIS, $CIFR, $IREN, $APLD, $WULF, $CORZ, $CRWV Compute capacity is overflow layer when hyperscalers are sold out. Capital alone doesn't guarantee GPU access which is why buyers are signing multi-year contracts for clusters before they are even fully built. • CPU | $NVDA, $AMD, $INTC, $ARM, $QCOM On-device CPU (edge compute) becomes next bottleneck as AI moves into inference, agents, PCs, phones, vehicles and physical devices.
1:58

THE 5 BIGGEST BOTTLENECKS POWERING THE AI ECONOMY The way I'm thinking about AI winners today is that the market is moving beyond the simple question of which mega-cap company spends most on AI and has been rewarding the companies that control the scarce inputs, contracted capacity, data movement, power infrastructure, edge compute and workflow layers that make the AI economy function. These are the five bottlenecks I'm watching most: • Memory | $MU, Samsung, SK Hynix Memory is the clearest scarce input because HBM feeds the accelerator, only a few companies can make it at volume and buyers are locking in supply through long-term agreements that create revenue visibility through the end of the decade. • Connectivity | $AVGO, $MRVL, $ALAB, $CRDO, $AAOI, $ANET Connectivity determines whether AI clusters can move data fast enough because training runs span tens of thousands of chips that need to act like one machine. Once copper runs out of reach that causes the cluster depends on optics, retimers, switches and custom silicon to keep the system moving. • Power | $CEG, $VST, $GEV, $FPS, $VRT, $NVTS, $TLN, $ON Power determines whether new AI data centers can actually come online because the binding constraint is shifting from getting chips to getting megawatts so the value flows to the companies that control generation, grid equipment, power delivery, thermal management and efficiency. • Compute | $NBIS, $CIFR, $IREN, $APLD, $WULF, $CORZ, $CRWV Compute capacity is overflow layer when hyperscalers are sold out. Capital alone doesn't guarantee GPU access which is why buyers are signing multi-year contracts for clusters before they are even fully built. • CPU | $NVDA, $AMD, $INTC, $ARM, $QCOM On-device CPU (edge compute) becomes next bottleneck as AI moves into inference, agents, PCs, phones, vehicles and physical devices.

Shay Boloor

109,467 Aufrufe • vor 2 Monaten

🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.
0:45

🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.

Black Panther Capital

58,387 Aufrufe • vor 7 Monaten

Markets are pulling back, but this is the time to buy MORE in my opinion. Don't be scared, don't be a little baby. 1. Indicies 0:00 - S&P 500, Nasdaq, Bitcoin, Gold/Silver, DRAM, Energy 2. Big Tech 7:11 - AAPL, AMZN, GOOGL, DELL, INTC, META, SPCX, MU, NFLX, PLTR, MSFT, AMD, NVDA, TSLA, BABA, NOW 3. Buy the Dip Setups 17:21 - ABCL, NEE, DE, TEM, DT, XBI, GH, ANET, ADI, MRK, ARKG, V, MA, OKTA, KMX, BMNR, COF, TMUS, TGT, SJM 4. Bottom Setups 24:06 - BEAM, QCOM, ARM, USAR, FORM, SMCI, CRWV, OKLO, RKT, VST, MDB, WING, WYFI, MSTR, PGR, SPGI, EOSE, MDLZ 5. Retail Favorites 29:38 - BE, SOFI, SMR, ONDS, IREN, NBIS, ZETA, IONQ All my buy and sell signals in Discord @
33:09

Markets are pulling back, but this is the time to buy MORE in my opinion. Don't be scared, don't be a little baby. 1. Indicies 0:00 - S&P 500, Nasdaq, Bitcoin, Gold/Silver, DRAM, Energy 2. Big Tech 7:11 - AAPL, AMZN, GOOGL, DELL, INTC, META, SPCX, MU, NFLX, PLTR, MSFT, AMD, NVDA, TSLA, BABA, NOW 3. Buy the Dip Setups 17:21 - ABCL, NEE, DE, TEM, DT, XBI, GH, ANET, ADI, MRK, ARKG, V, MA, OKTA, KMX, BMNR, COF, TMUS, TGT, SJM 4. Bottom Setups 24:06 - BEAM, QCOM, ARM, USAR, FORM, SMCI, CRWV, OKLO, RKT, VST, MDB, WING, WYFI, MSTR, PGR, SPGI, EOSE, MDLZ 5. Retail Favorites 29:38 - BE, SOFI, SMR, ONDS, IREN, NBIS, ZETA, IONQ All my buy and sell signals in Discord @

StockWhale

37,149 Aufrufe • vor 1 Tag

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.
0:41

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.

The Assembly

7,303,182 Aufrufe • vor 3 Monaten

The AI trade can actually be simple if you want it to be. Just follow what $NVDA (Jensen) and the US Gov are literally telling you to do: 1. Are you covered on photonics/EML & interconnect? i.e. $NVDA bought up $LITE, $COHR, and $MRVL. Reminder that Jensen said $MRVL is the next trillion dollar company. 2. Are you covered on neoclouds? i.e. Jensen said $NBIS will "take care of you" 3. Are you covered on rare earths? i.e. White House own $USAR & $MP. 4. Are you covered on the copper, lithium & scandium trades? i.e. White House own $TMQ, $LAC, and $SRL 5. Are you covered on quantum? i.e. White House have small stakes in 9 quantum players ($IBM, $GFS, $INFQ etc) 6. Are you covered on the power generation trade? i.e. Jensen has been very bullish on nuclear and SMRs ($OKLO, $SMR etc). 7. Are you covered in the digital biology trade? i.e. Jensen has said this could be the place with the most disruption over the next decade ($LLY, $TMO, $SDGR) I could go on but I think you get the idea.
1:20

The AI trade can actually be simple if you want it to be. Just follow what $NVDA (Jensen) and the US Gov are literally telling you to do: 1. Are you covered on photonics/EML & interconnect? i.e. $NVDA bought up $LITE, $COHR, and $MRVL. Reminder that Jensen said $MRVL is the next trillion dollar company. 2. Are you covered on neoclouds? i.e. Jensen said $NBIS will "take care of you" 3. Are you covered on rare earths? i.e. White House own $USAR & $MP. 4. Are you covered on the copper, lithium & scandium trades? i.e. White House own $TMQ, $LAC, and $SRL 5. Are you covered on quantum? i.e. White House have small stakes in 9 quantum players ($IBM, $GFS, $INFQ etc) 6. Are you covered on the power generation trade? i.e. Jensen has been very bullish on nuclear and SMRs ($OKLO, $SMR etc). 7. Are you covered in the digital biology trade? i.e. Jensen has said this could be the place with the most disruption over the next decade ($LLY, $TMO, $SDGR) I could go on but I think you get the idea.

Oliver | MMMT Wealth (CPA)

17,760 Aufrufe • vor 16 Tagen

Leopold Aschenbrenner has approached existing investors for fresh capital after the ongoing selloff. "We call out opportunities that seem like a particularly good time to add funds if you have been waiting for one." Here's what the fund was holding when the drawdown hit. His long book leans on the physical layer of AI rather than the software layer: chips, power, and data centers. $BE is reportedly his single largest position with $NBIS and $AMD also named among his holdings alongside $CRWV, $SNDK and $ORCL. Two of those names are not new information to our team. Our PRO analysts bought $BE and $NBIS as core positions way back in February. Aschenbrenner is now trying to raise fresh money to buy more of: - AI infrastructure stocks - Physical bottlenecks (power, memory, and compute) Our PRO analysts are tracking this very closely and are making trades around this thesis. If you want complete access to their portfolios, come join us for $1. Link in the first comment below.
0:56

Leopold Aschenbrenner has approached existing investors for fresh capital after the ongoing selloff. "We call out opportunities that seem like a particularly good time to add funds if you have been waiting for one." Here's what the fund was holding when the drawdown hit. His long book leans on the physical layer of AI rather than the software layer: chips, power, and data centers. $BE is reportedly his single largest position with $NBIS and $AMD also named among his holdings alongside $CRWV, $SNDK and $ORCL. Two of those names are not new information to our team. Our PRO analysts bought $BE and $NBIS as core positions way back in February. Aschenbrenner is now trying to raise fresh money to buy more of: - AI infrastructure stocks - Physical bottlenecks (power, memory, and compute) Our PRO analysts are tracking this very closely and are making trades around this thesis. If you want complete access to their portfolios, come join us for $1. Link in the first comment below.

Milk Road Stocks

68,580 Aufrufe • vor 1 Monat

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026
0:49

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

Mike

145,992 Aufrufe • vor 3 Monaten

🚨SoSoValue Flash: Great Power Diplomacy & Liquidity Squeeze, Markets Brace for NVDA Earnings 💥 Core Catalyst: Truce Extensions & Tehran ShadowsTrump has warned of fresh strikes within 2–3 days if no deal is reached (potentially Friday–Sunday or early next week), though the market continues to price in "TACO" (Talk-and-Carry-On) as the base case. On Monday night, Vladimir Putin landed in Beijing ahead of his summit with Xi, signaling deepening China-Russia coordination. 🔍 Key Logic Shifts: 1️⃣ Macro & Yields: The 10Y U.S. Treasury yield briefly neared 4.7% as major sell-side desks lifted targets above 4.6%, triggering a wave of stop-loss selling. G7 finance ministers, including Bessent, provided no rate-soothing signals, deflating expectations for a "policy backstop" and further pressuring liquidity. 2️⃣ AI Portfolio: The "is AI topping?" debate continues to build, but strong industry trends and earnings fundamentals argue against a structural reversal—a pullback remains the more likely scenario. 3️⃣ Event Pivot: NVIDIA’s (NVDA) earnings on Wednesday after the bell stand as this week's primary pivot. With expectations at a fever pitch, the results are set to be the dominant source of market volatility. 📊 Trade Setup (SoDEX Assets to Watch): Core: $USTECH-100 | $CL (Crude) | $XAUT | $BTC MAG7: $NVDA | $AMZN | $GOOGL | $META | $MSFT | $TSLA | $AAPL AI Hardware: $SNDK | $MU | $AMD | $INTC
1:17

🚨SoSoValue Flash: Great Power Diplomacy & Liquidity Squeeze, Markets Brace for NVDA Earnings 💥 Core Catalyst: Truce Extensions & Tehran ShadowsTrump has warned of fresh strikes within 2–3 days if no deal is reached (potentially Friday–Sunday or early next week), though the market continues to price in "TACO" (Talk-and-Carry-On) as the base case. On Monday night, Vladimir Putin landed in Beijing ahead of his summit with Xi, signaling deepening China-Russia coordination. 🔍 Key Logic Shifts: 1️⃣ Macro & Yields: The 10Y U.S. Treasury yield briefly neared 4.7% as major sell-side desks lifted targets above 4.6%, triggering a wave of stop-loss selling. G7 finance ministers, including Bessent, provided no rate-soothing signals, deflating expectations for a "policy backstop" and further pressuring liquidity. 2️⃣ AI Portfolio: The "is AI topping?" debate continues to build, but strong industry trends and earnings fundamentals argue against a structural reversal—a pullback remains the more likely scenario. 3️⃣ Event Pivot: NVIDIA’s (NVDA) earnings on Wednesday after the bell stand as this week's primary pivot. With expectations at a fever pitch, the results are set to be the dominant source of market volatility. 📊 Trade Setup (SoDEX Assets to Watch): Core: $USTECH-100 | $CL (Crude) | $XAUT | $BTC MAG7: $NVDA | $AMZN | $GOOGL | $META | $MSFT | $TSLA | $AAPL AI Hardware: $SNDK | $MU | $AMD | $INTC

SoSoValue

54,129 Aufrufe • vor 3 Monaten

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.
0:58

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.

Ricardo

594,643 Aufrufe • vor 7 Monaten

WHAT IF CHINA INVADES TAIWAN + Strait of Hormuz STAYS CLOSED🛑 Nobody is connecting these dots yet Taiwan makes 90% of the world's most advanced chips. TSMC manufactures for EVERY major tech company on the planet. If those fabs go dark, this isn't a dip. This is a STRUCTURAL COLLAPSE. Stocks that get DESTROYED 👇 Semiconductors. $TSM, $NVDA, $AMD, $AAPL, $QCOM, $AVGO, $ASML. Every single one depends on TSMC to make their chips. There is NO backup. Nvidia can't make GPUs. Apple can't make iPhones. AMD can't make processors. Full stop. The AI boom DIES overnight. $MSFT, $AMZN, $GOOG, $META. Every data center buildout, every model training run, every hyperscaler capex plan depends on chips that ONLY TSMC can produce at scale. Their entire growth thesis is GONE. Consumer and shipping. $DELL, $HPQ, Sony all lose their chip supply. The Taiwan Strait carries 50% of global container shipping. $ZIM, $FDX, $UPS all get crushed by the blockade. NOW here's where it gets interesting Stocks that BENEFIT 👇 Defense. $LMT, $RTX, $NOC, $GD, $LHX, $PLTR. Spending goes VERTICAL overnight. Domestic chips. $INTC becomes the most important company in America as the ONLY Western advanced fab. $GFS, $AMAT, $LRCX, $KLAC all surge as we scramble to build domestic capacity. Safe havens. $XOM, $CVX on the oil spike. $GLD, $SLV on the flight to safety. $AA on the aluminum supply chain chaos. THE PART NOBODY IS TALKING ABOUT Taiwan doesn't just make chips for tech. They produce 35% of ALL chips globally. Cars. Medical devices. Military equipment. Appliances. A Taiwan invasion doesn't crash the stock market. It crashes the GLOBAL ECONOMY. This is not a prediction. But if you're not thinking about this risk while the Strait of Hormuz is ALREADY shut down and semiconductors are ALREADY under pressure from helium shortages, you're not paying attention.
5:56

WHAT IF CHINA INVADES TAIWAN + Strait of Hormuz STAYS CLOSED🛑 Nobody is connecting these dots yet Taiwan makes 90% of the world's most advanced chips. TSMC manufactures for EVERY major tech company on the planet. If those fabs go dark, this isn't a dip. This is a STRUCTURAL COLLAPSE. Stocks that get DESTROYED 👇 Semiconductors. $TSM, $NVDA, $AMD, $AAPL, $QCOM, $AVGO, $ASML. Every single one depends on TSMC to make their chips. There is NO backup. Nvidia can't make GPUs. Apple can't make iPhones. AMD can't make processors. Full stop. The AI boom DIES overnight. $MSFT, $AMZN, $GOOG, $META. Every data center buildout, every model training run, every hyperscaler capex plan depends on chips that ONLY TSMC can produce at scale. Their entire growth thesis is GONE. Consumer and shipping. $DELL, $HPQ, Sony all lose their chip supply. The Taiwan Strait carries 50% of global container shipping. $ZIM, $FDX, $UPS all get crushed by the blockade. NOW here's where it gets interesting Stocks that BENEFIT 👇 Defense. $LMT, $RTX, $NOC, $GD, $LHX, $PLTR. Spending goes VERTICAL overnight. Domestic chips. $INTC becomes the most important company in America as the ONLY Western advanced fab. $GFS, $AMAT, $LRCX, $KLAC all surge as we scramble to build domestic capacity. Safe havens. $XOM, $CVX on the oil spike. $GLD, $SLV on the flight to safety. $AA on the aluminum supply chain chaos. THE PART NOBODY IS TALKING ABOUT Taiwan doesn't just make chips for tech. They produce 35% of ALL chips globally. Cars. Medical devices. Military equipment. Appliances. A Taiwan invasion doesn't crash the stock market. It crashes the GLOBAL ECONOMY. This is not a prediction. But if you're not thinking about this risk while the Strait of Hormuz is ALREADY shut down and semiconductors are ALREADY under pressure from helium shortages, you're not paying attention.

JEFE TRADES 🔪

222,093 Aufrufe • vor 6 Monaten

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.
0:49

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.

Mike

511,082 Aufrufe • vor 10 Monaten

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!
2:25

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

Mike

104,109 Aufrufe • vor 3 Monaten

Leopold Aschenbrenner is a 24 year old who turned $225 million into $5.5 billion in just 12 months. His 13F just dropped and it reveals exactly where he's putting that money. Top holdings, all brand new this quarter: SMH (VanEck Semi ETF): $2.04B NVDA: $1.57B ORCL: $1.07B AVGO: $1.01B AMD: $969M MU: $584M TSM: $535M ASML: $494M INTC: $159M But the most interesting move isn't what he bought. It's what he flipped. Last quarter he held a $747M bullish call on Intel. This quarter that position is now a put. He also cut his CoreWeave position from $774M down to $141M. Full exits: Lumentum, Coherent, Tower Semiconductor, Cipher Mining, Hut 8. What he kept and added: the bitcoin miners turning themselves into AI data centers. Applied Digital, Bitfarms, CleanSpark, IREN, Riot. Plus a brand new position in Hive Digital. The thesis writes itself: Long the companies pouring the concrete for the data centers. Cutting exposure to the chips going inside them. The man who wrote the definitive manifesto on AGI just faded the semiconductors building it. Our analysts are watching every 13F release and positioning accordingly. They were early to $AMD, early to $MU, early to $BE. Don’t miss the next call, come join us for just a $1. (link in bio)
4:06

Leopold Aschenbrenner is a 24 year old who turned $225 million into $5.5 billion in just 12 months. His 13F just dropped and it reveals exactly where he's putting that money. Top holdings, all brand new this quarter: SMH (VanEck Semi ETF): $2.04B NVDA: $1.57B ORCL: $1.07B AVGO: $1.01B AMD: $969M MU: $584M TSM: $535M ASML: $494M INTC: $159M But the most interesting move isn't what he bought. It's what he flipped. Last quarter he held a $747M bullish call on Intel. This quarter that position is now a put. He also cut his CoreWeave position from $774M down to $141M. Full exits: Lumentum, Coherent, Tower Semiconductor, Cipher Mining, Hut 8. What he kept and added: the bitcoin miners turning themselves into AI data centers. Applied Digital, Bitfarms, CleanSpark, IREN, Riot. Plus a brand new position in Hive Digital. The thesis writes itself: Long the companies pouring the concrete for the data centers. Cutting exposure to the chips going inside them. The man who wrote the definitive manifesto on AGI just faded the semiconductors building it. Our analysts are watching every 13F release and positioning accordingly. They were early to $AMD, early to $MU, early to $BE. Don’t miss the next call, come join us for just a $1. (link in bio)

Milk Road Macro

313,372 Aufrufe • vor 3 Monaten

The neocloud category may be the most misunderstood corner of the AI trade because the market still treats these names as one uniform GPU-hours bet when they are actually very different business models: 1. $NBIS (Cloud Utility for the Agentic AI Age) $NVDA just chose Nebius as an architecture partner for the agentic AI era by co-designing AI factories with them, and the Rubin GPU access that comes with this partnership means Nebius gets the next-generation inference stack before almost anyone else in the market. At a $28B market cap, a 5GW power target and Nvidia’s engineering team embedded in the stack.. this is my favorite name in the neocloud category. 2. $IREN (Energy-to-Compute Engine of the AI Era) The dilution fear is real but the market is misreading it. IREN is not diluting to survive but diluting to scale into a $3.7B ARR target and the $9.3B in funding already secured through customer prepayments and GPU financing means the $6B ATM is optionality capital. The real bottleneck in AI infrastructure right now is power and IREN controls ~4.5GW of secured capacity while needing only ~500MW to support its ARR target by year-end. That 10x ratio of power capacity to near-term need is something no competitor can replicate quickly. 3. $CIFR (Landlord of the AI Utility Era) Cipher is not a pure neocloud but is a hyperscale infrastructure landlord signing decade-long leases to $AMZN AWS and $GOOGL while they fill the shells with compute. The AWS lease alone is expected to generate ~$700M in average annualized NOI for the next decade at nearly 100% NOI margins. Power-rich land is the scarcest resource in AI infrastructure and Cipher controls it with 600MW fully contracted, both facilities fully funded through non-recourse fixed-rate project debt and a 3.4GW development pipeline. 4. $CRWV (The Fragile Giant) CoreWeave’s demand backlog and revenue growth are very real but none of that matters if the capital markets close for even one quarter. Interest expense hit $388M in Q4 and management guided Q1 2026 interest expense to ~$550M which implies an annualized run rate above $2B before a single new data center comes online. The bull case requires capital markets to stay open, rates to cooperate, hyperscalers to honor take-or-pay contracts in full and construction to stay on time. That is a lot of dependencies in a macro environment where oil is approaching $100 and private credit is already showing signs of stress.
1:39

The neocloud category may be the most misunderstood corner of the AI trade because the market still treats these names as one uniform GPU-hours bet when they are actually very different business models: 1. $NBIS (Cloud Utility for the Agentic AI Age) $NVDA just chose Nebius as an architecture partner for the agentic AI era by co-designing AI factories with them, and the Rubin GPU access that comes with this partnership means Nebius gets the next-generation inference stack before almost anyone else in the market. At a $28B market cap, a 5GW power target and Nvidia’s engineering team embedded in the stack.. this is my favorite name in the neocloud category. 2. $IREN (Energy-to-Compute Engine of the AI Era) The dilution fear is real but the market is misreading it. IREN is not diluting to survive but diluting to scale into a $3.7B ARR target and the $9.3B in funding already secured through customer prepayments and GPU financing means the $6B ATM is optionality capital. The real bottleneck in AI infrastructure right now is power and IREN controls ~4.5GW of secured capacity while needing only ~500MW to support its ARR target by year-end. That 10x ratio of power capacity to near-term need is something no competitor can replicate quickly. 3. $CIFR (Landlord of the AI Utility Era) Cipher is not a pure neocloud but is a hyperscale infrastructure landlord signing decade-long leases to $AMZN AWS and $GOOGL while they fill the shells with compute. The AWS lease alone is expected to generate ~$700M in average annualized NOI for the next decade at nearly 100% NOI margins. Power-rich land is the scarcest resource in AI infrastructure and Cipher controls it with 600MW fully contracted, both facilities fully funded through non-recourse fixed-rate project debt and a 3.4GW development pipeline. 4. $CRWV (The Fragile Giant) CoreWeave’s demand backlog and revenue growth are very real but none of that matters if the capital markets close for even one quarter. Interest expense hit $388M in Q4 and management guided Q1 2026 interest expense to ~$550M which implies an annualized run rate above $2B before a single new data center comes online. The bull case requires capital markets to stay open, rates to cooperate, hyperscalers to honor take-or-pay contracts in full and construction to stay on time. That is a lot of dependencies in a macro environment where oil is approaching $100 and private credit is already showing signs of stress.

Shay Boloor

1,098,185 Aufrufe • vor 6 Monaten

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!
7:18

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!

Mike

141,018 Aufrufe • vor 10 Monaten

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!
1:06

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!

Mike

711,006 Aufrufe • vor 11 Monaten