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$SKHY has the cleanest current exposure to the memory segment where $NVDA roadmap is creating the most value. If HBM content per AI rack climbs from the low hundreds of thousands today to potentially more than $1M with Rubin Ultra then SK Hynix becomes a direct bet on structural... show more

Shay Boloor

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201,644 Aufrufe • vor 14 Tagen •via X (Twitter)

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$MU hit a $1T valuation today because the market is waking up to the idea that memory may actually become one of the core infrastructure layers of the AI economy. The catalyst was SK Hynix unveiling iHBM which is a new cooling technology for next-gen HBM but the bigger point is that token growth, AI factories, inference demand & next-gen accelerators all require more HBM per system. More HBM per accelerator means more memory dollar content & with only three major HBM suppliers in the world the entire memory complex is getting re-rated.

$MU hit a $1T valuation today because the market is waking up to the idea that memory may actually become one of the core infrastructure layers of the AI economy. The catalyst was SK Hynix unveiling iHBM which is a new cooling technology for next-gen HBM but the bigger point is that token growth, AI factories, inference demand & next-gen accelerators all require more HBM per system. More HBM per accelerator means more memory dollar content & with only three major HBM suppliers in the world the entire memory complex is getting re-rated.

Shay Boloor

306,107 Aufrufe • vor 1 Monat

Sunrise has listed $SKHY on @Solana. SK Hynix is the leading supplier of the HBM behind AI compute. Today is its first US listing. Issued by Backpack 🎒 Securities, $SKHY brings a tokenized SK Hynix share onchain. Sunrise makes it tradable across Solana wallets and app, 24/7.

Sunrise has listed $SKHY on @Solana. SK Hynix is the leading supplier of the HBM behind AI compute. Today is its first US listing. Issued by Backpack 🎒 Securities, $SKHY brings a tokenized SK Hynix share onchain. Sunrise makes it tradable across Solana wallets and app, 24/7.

Sunrise

165,031 Aufrufe • vor 14 Tagen

Teaming up with SanDisk to build the next standard for AI inference. As a full-stack AI memory creator, SK hynix is launching the global standardization of #HBF with Sandisk & Open Compute Project (OCP). HBF is the new memory layer bridging the gap between #HBM and #SSD. #SKhynix

Teaming up with SanDisk to build the next standard for AI inference. As a full-stack AI memory creator, SK hynix is launching the global standardization of #HBF with Sandisk & Open Compute Project (OCP). HBF is the new memory layer bridging the gap between #HBM and #SSD. #SKhynix

SK hynix

194,727 Aufrufe • vor 4 Monaten

SK hynix became a core AI player with #HBM. What did it take to meet @NVIDIA’s ambitious requirements and move faster than ever? 👀 From #HBM4 acceleration to hardcore collaboration — here’s the story. 📌 Full video: #SKhynix #AI #Memory #SuperMomentum

SK hynix became a core AI player with #HBM. What did it take to meet @NVIDIA’s ambitious requirements and move faster than ever? 👀 From #HBM4 acceleration to hardcore collaboration — here’s the story. 📌 Full video: #SKhynix #AI #Memory #SuperMomentum

SK hynix

19,365 Aufrufe • vor 5 Monaten

If $MU says NAND pricing is still strong, enterprise SSD demand is picking up and AI inference is creating more storage demand then $SNDK should benefit because SanDisk is one of the more direct ways to own that part of the cycle. Hyperscalers are still figuring out the right mix of DRAM, HBM, TLC NAND, QLC NAND and other storage architectures which can keep supply tighter for longer as memory companies are going to be hesitant to add too much capacity before demand becomes clearer. I'm going to be listening closely for Micron’s QLC commentary because QLC NAND for read-heavy inference workloads is the big driver and if Micron calls that out then it would show that the storage side of the AI memory story is still earlier than DRAM.

If $MU says NAND pricing is still strong, enterprise SSD demand is picking up and AI inference is creating more storage demand then $SNDK should benefit because SanDisk is one of the more direct ways to own that part of the cycle. Hyperscalers are still figuring out the right mix of DRAM, HBM, TLC NAND, QLC NAND and other storage architectures which can keep supply tighter for longer as memory companies are going to be hesitant to add too much capacity before demand becomes clearer. I'm going to be listening closely for Micron’s QLC commentary because QLC NAND for read-heavy inference workloads is the big driver and if Micron calls that out then it would show that the storage side of the AI memory story is still earlier than DRAM.

Shay Boloor

108,953 Aufrufe • vor 1 Monat

Gavin Baker, CIO of Atreides Management made one of the most important and nuanced calls on memory stocks in recent months (Save this). His argument is that based on every memory cycle of the last 25 years, the setup today, prices elevated, sentiment high, supply ramping is textbook time to sell but he adds a critical exception. The one cycle in modern memory history where selling was catastrophically wrong was the mid-1990s, which Baker calls the last true capacity cycle in memory. In that cycle, demand was structurally exploding as the internet era required entirely new computing infrastructure to be built from scratch, and memory had to scale with it in a way that had never happened before. His point is that AI may be that same kind of cycle and not a normal boom bust but a once in a generation capacity buildout where the underlying demand is structural, not cyclical. The reason this argument holds weight is the fundamental shift in what memory is in the AI era. Traditional DRAM was a pure commodity, identical specs, interchangeable suppliers, price determined entirely by supply and demand swings. HBM is the opposite because it is custom engineered to fit a specific customer's chip, co-designed between the memory maker and the GPU designer, with SK Hynix's Vice President literally describing it as shifting from a commodity to a customer-tailored custom business. A single Blackwell Ultra GPU now requires up to 288GB of HBM3E, a 3.6x increase over the H100 and major suppliers like SK Hynix and Micron have already sold out their entire HBM production capacity through the end of the year. Because HBM requires advanced packaging processes like CoWoS that can't be spun up overnight, the bottleneck isn't just wafer capacity but rather runs across the entire manufacturing stack. Bank of America projects the global HBM market grows 58% this year alone to $54.6 billion, and Nomura expects the broader memory sector to nearly double to $445 billion. Long Micron!

Gavin Baker, CIO of Atreides Management made one of the most important and nuanced calls on memory stocks in recent months (Save this). His argument is that based on every memory cycle of the last 25 years, the setup today, prices elevated, sentiment high, supply ramping is textbook time to sell but he adds a critical exception. The one cycle in modern memory history where selling was catastrophically wrong was the mid-1990s, which Baker calls the last true capacity cycle in memory. In that cycle, demand was structurally exploding as the internet era required entirely new computing infrastructure to be built from scratch, and memory had to scale with it in a way that had never happened before. His point is that AI may be that same kind of cycle and not a normal boom bust but a once in a generation capacity buildout where the underlying demand is structural, not cyclical. The reason this argument holds weight is the fundamental shift in what memory is in the AI era. Traditional DRAM was a pure commodity, identical specs, interchangeable suppliers, price determined entirely by supply and demand swings. HBM is the opposite because it is custom engineered to fit a specific customer's chip, co-designed between the memory maker and the GPU designer, with SK Hynix's Vice President literally describing it as shifting from a commodity to a customer-tailored custom business. A single Blackwell Ultra GPU now requires up to 288GB of HBM3E, a 3.6x increase over the H100 and major suppliers like SK Hynix and Micron have already sold out their entire HBM production capacity through the end of the year. Because HBM requires advanced packaging processes like CoWoS that can't be spun up overnight, the bottleneck isn't just wafer capacity but rather runs across the entire manufacturing stack. Bank of America projects the global HBM market grows 58% this year alone to $54.6 billion, and Nomura expects the broader memory sector to nearly double to $445 billion. Long Micron!

Milk Road AI

260,588 Aufrufe • vor 25 Tagen

$MU $00060 $00530 $TSM Evolution of the AI Memory Hierarchy: HBM, DRAM, & SRAM Overview and Market Analysis The video outlines the strategic shift of modern computing toward a memory-centric architecture driven by the demands of generative AI. It explains that the industry is moving away from a traditional commodity model to a tightly coupled hierarchy of specialized technologies, where High Bandwidth Memory (HBM) and advanced packaging act as primary bottlenecks for performance. Leading manufacturers like SK hynix, Samsung, and Micron are navigating complex transitions to HBM4 and CXL-based expansion to overcome data movement constraints. Beyond the memory producers, the ecosystem relies on integration enablers like TSMC for packaging and specialized equipment providers for high-precision manufacturing. Ultimately, the analysis suggests that memory intensity and power efficiency have become the decisive factors in the success of gigawatt-scale data centers.

$MU $00060 $00530 $TSM Evolution of the AI Memory Hierarchy: HBM, DRAM, & SRAM Overview and Market Analysis The video outlines the strategic shift of modern computing toward a memory-centric architecture driven by the demands of generative AI. It explains that the industry is moving away from a traditional commodity model to a tightly coupled hierarchy of specialized technologies, where High Bandwidth Memory (HBM) and advanced packaging act as primary bottlenecks for performance. Leading manufacturers like SK hynix, Samsung, and Micron are navigating complex transitions to HBM4 and CXL-based expansion to overcome data movement constraints. Beyond the memory producers, the ecosystem relies on integration enablers like TSMC for packaging and specialized equipment providers for high-precision manufacturing. Ultimately, the analysis suggests that memory intensity and power efficiency have become the decisive factors in the success of gigawatt-scale data centers.

TheValueist

23,901 Aufrufe • vor 7 Monaten

Elon Musk just described a project so large that most people will assume he is exaggerating (Save this). He is not. In the video, Musk lays out the central problem facing every AI company on earth, the entire global chip industry is on a path to produce roughly 100 gigawatts of AI compute per year. That sounds like a lot until you understand that his companies alone Tesla, SpaceX, and xAI will need orders of magnitude more than that. His answer is the TerraFab. It is a joint chip factory spanning 100 million square feet, ten times the size of Tesla's Gigafactory Texas announced in March 2026, with Grimes County, Texas commissioners approving the full scale facility site just last week. The goal is one full terawatt of AI compute output per year. For context, 1 terawatt is 1,000 gigawatts twice the current total electricity consumption of the United States. SpaceX has already committed an initial $55 billion to the prototype phase, with total investment estimates ranging into the trillions. Here is why this matters for Micron specifically. In the video, Musk named Nvidia's Rubin chips as the reference design for TerraFab's first orbital deployments, and said "You're going to need a lot of memory to go with that." A billion full radical equivalent chips per year, each requiring stacks of high bandwidth memory, that is the demand signal Micron just received from one of the most capital-intensive projects in human history. And Micron already cannot keep up with what exists today. Micron's entire 2026 HBM output is fully sold out contracted before the year began. HBM4 entered volume production ahead of schedule and sold out immediately. The structural reason Micron wins here is simple. Every AI chip ever built Nvidia H100s, Rubin chips, custom ASICs, TPUs is useless without high-bandwidth memory stacked directly on top of it. There are only three companies in the world that supply HBM at scale, Samsung, SK Hynix, and Micron. Samsung has had quality issues, SK Hynix is supply constrained. Micron is the only US headquartered HBM manufacturer which matters enormously given CHIPS Act subsidies, domestic procurement requirements, and the political push to keep critical AI memory production on American soil. TerraFab just made the memory deficit permanently larger. Come join Milk Road Pro for our full breakdown of Micron and our entire AI thesis just for $1. Link below!

Elon Musk just described a project so large that most people will assume he is exaggerating (Save this). He is not. In the video, Musk lays out the central problem facing every AI company on earth, the entire global chip industry is on a path to produce roughly 100 gigawatts of AI compute per year. That sounds like a lot until you understand that his companies alone Tesla, SpaceX, and xAI will need orders of magnitude more than that. His answer is the TerraFab. It is a joint chip factory spanning 100 million square feet, ten times the size of Tesla's Gigafactory Texas announced in March 2026, with Grimes County, Texas commissioners approving the full scale facility site just last week. The goal is one full terawatt of AI compute output per year. For context, 1 terawatt is 1,000 gigawatts twice the current total electricity consumption of the United States. SpaceX has already committed an initial $55 billion to the prototype phase, with total investment estimates ranging into the trillions. Here is why this matters for Micron specifically. In the video, Musk named Nvidia's Rubin chips as the reference design for TerraFab's first orbital deployments, and said "You're going to need a lot of memory to go with that." A billion full radical equivalent chips per year, each requiring stacks of high bandwidth memory, that is the demand signal Micron just received from one of the most capital-intensive projects in human history. And Micron already cannot keep up with what exists today. Micron's entire 2026 HBM output is fully sold out contracted before the year began. HBM4 entered volume production ahead of schedule and sold out immediately. The structural reason Micron wins here is simple. Every AI chip ever built Nvidia H100s, Rubin chips, custom ASICs, TPUs is useless without high-bandwidth memory stacked directly on top of it. There are only three companies in the world that supply HBM at scale, Samsung, SK Hynix, and Micron. Samsung has had quality issues, SK Hynix is supply constrained. Micron is the only US headquartered HBM manufacturer which matters enormously given CHIPS Act subsidies, domestic procurement requirements, and the political push to keep critical AI memory production on American soil. TerraFab just made the memory deficit permanently larger. Come join Milk Road Pro for our full breakdown of Micron and our entire AI thesis just for $1. Link below!

Milk Road AI

245,900 Aufrufe • vor 1 Monat

🚨 WHAT IF THE MEMORY SHORTAGE WAS MANUFACTURED? Samsung, SK Hynix, and Micron are being sued for allegedly engineering the DRAM shortage on purpose. The lawsuit claims they used the AI boom as cover to quietly cut normal memory supply. DRAM prices are up 500 to 700%. The damage is now showing up in iPhone and Mac prices. The makers say it's innocent: AI buyers showed up with huge money for HBM. But here's the bigger question. Memory stocks are priced for endless AI demand. If a court rules the scarcity was manufactured, how much of that demand was ever real?

🚨 WHAT IF THE MEMORY SHORTAGE WAS MANUFACTURED? Samsung, SK Hynix, and Micron are being sued for allegedly engineering the DRAM shortage on purpose. The lawsuit claims they used the AI boom as cover to quietly cut normal memory supply. DRAM prices are up 500 to 700%. The damage is now showing up in iPhone and Mac prices. The makers say it's innocent: AI buyers showed up with huge money for HBM. But here's the bigger question. Memory stocks are priced for endless AI demand. If a court rules the scarcity was manufactured, how much of that demand was ever real?

CryptoGoos

32,262 Aufrufe • vor 24 Tagen

🚨 THIS IS NOT NORMAL 🇺🇸 OVER $400,000,000,000.00 HAS BEEN WIPED OUT FROM THE U.S. STOCK MARKET IN JUST 5 MINUTES! MEMORY CHIP MAKERS SAMSUNG AND SK HYNIX ARE DOWN 10–15% AND CONTINUE TO FALL. LOOKS LIKE THE AI BUBBLE IS OFFICIALLY STARTING TO BURST...

🚨 THIS IS NOT NORMAL 🇺🇸 OVER $400,000,000,000.00 HAS BEEN WIPED OUT FROM THE U.S. STOCK MARKET IN JUST 5 MINUTES! MEMORY CHIP MAKERS SAMSUNG AND SK HYNIX ARE DOWN 10–15% AND CONTINUE TO FALL. LOOKS LIKE THE AI BUBBLE IS OFFICIALLY STARTING TO BURST...

0xNobler

59,889 Aufrufe • vor 11 Tagen

KOSPI went from 3,033 to 9,386 and back into a bear market inside eleven months. Seven circuit breaker halts so far this year, surpassing 2008. The memory shortage is real in the short to medium term, Samsung $SSNLF and SK Hynix $SKHY sit at the center of it, and the index may well run again from here. But a country index that triples on one industry's capex is not a stock market anymore. It is a single trade with a country attached :)

KOSPI went from 3,033 to 9,386 and back into a bear market inside eleven months. Seven circuit breaker halts so far this year, surpassing 2008. The memory shortage is real in the short to medium term, Samsung $SSNLF and SK Hynix $SKHY sit at the center of it, and the index may well run again from here. But a country index that triples on one industry's capex is not a stock market anymore. It is a single trade with a country attached :)

Inferam

32,107 Aufrufe • vor 7 Tagen

What if the AI bubble was built on an engineered memory shortage? $MU just crashed over 17% from its recent high right as Micron, Samsung, and SK Hynix got hit with a federal lawsuit accusing them of restricting DRAM supply while AI demand sent prices up nearly 700%. The scary part? Samsung and SK Hynix already pleaded guilty to memory chip price fixing once before. And now the entire AI boom runs on their chips.

What if the AI bubble was built on an engineered memory shortage? $MU just crashed over 17% from its recent high right as Micron, Samsung, and SK Hynix got hit with a federal lawsuit accusing them of restricting DRAM supply while AI demand sent prices up nearly 700%. The scary part? Samsung and SK Hynix already pleaded guilty to memory chip price fixing once before. And now the entire AI boom runs on their chips.

Crypto Rover

91,882 Aufrufe • vor 25 Tagen

As Phoenix AI Growth Points is expected to go live next week – PhoenixONE has leveled up once again in the meantime. Now, with the click of a button (new “Synthesize” feature) create full articles from entire conversations that go beyond in depth, quality, and data-driven rigor than much of the content on major media outlets. Acquire knowledge and insights, share content, and get rewarded in this new system. The more engagement and exposure your content gets, the bigger the reward. Create in-depth content in as little as a few minutes about anything – especially crypto! Phoenix is adding a social element to data-driven generative AI to drive user value and acceleration of growth. $PHB #Phoenix

As Phoenix AI Growth Points is expected to go live next week – PhoenixONE has leveled up once again in the meantime. Now, with the click of a button (new “Synthesize” feature) create full articles from entire conversations that go beyond in depth, quality, and data-driven rigor than much of the content on major media outlets. Acquire knowledge and insights, share content, and get rewarded in this new system. The more engagement and exposure your content gets, the bigger the reward. Create in-depth content in as little as a few minutes about anything – especially crypto! Phoenix is adding a social element to data-driven generative AI to drive user value and acceleration of growth. $PHB #Phoenix

Phoenix AI Labs

14,390 Aufrufe • vor 1 Jahr

The changes in the Algo and what Elon is truly trying to do - here is the full breakdown. A couple of Quick Points: - It all represents a really significant shift in what we’re about to see in our feed and the overall experience on X. - The shift from reply guy tactics to authenticity is where it’s all headed. - The value of a post will not be defined by engagement in its entirety but by the native value of the content. Current State: - Content visibility, including posts and replies, is currently unevenly prioritized, with manually written heuristics favoring recommendations to followers, benefitting large accounts but limiting the reach of smaller ones. - The "For You" algo heavily relies on these heuristics - impacting content reach and potentially hindering smaller accounts, regardless of content quality. Proposed Changes: - Algo defined value assignment to content will be driven by End-to-End Architecture based AI. - User-specific vectors, representing preferences, will guide content prioritization, providing a refreshed user experience within the feed that is inclusive and personalized. - The new approach will promote quality content across the entire user base, irrespective of account size. Additional Insights: - Elon emphasizes that AI will play a pivotal role in content importance assignment, suggesting a departure from the current manually-driven processes. - The algo will differentiate between content from accounts users follow (non-AI recommended) and those they don't (AI recommended), which should significantly improve the diversity of the content we’re exposed to. Implications: The proposed changes signify a fundamental transformation in how content is presented and recommended, with a move towards a more democratic and AI-driven system. Smaller accounts may benefit from increased visibility, creating a more level playing field based on content quality rather than follower count. Theories: - The mention of AI-differentiated recommendations for followed and non-followed content suggests a strategic effort to broaden user exposure to diverse content. - The shift towards an AI-powered recommendation system indicates a commitment to enhancing user experience by tailoring content suggestions based on individual preferences. What we’re looking at is a change that finally addresses the FOMO dynamic between accounts with reach and truly compelling content. It will be a major chance for new accounts, small accounts, and anyone really - to advance into the greater ecosphere they were left out of before. Goodbye, reply guy. Hello, value adding content. These conclusions are based on the recent interview of Elon Musk by Lex Fridman.

The changes in the Algo and what Elon is truly trying to do - here is the full breakdown. A couple of Quick Points: - It all represents a really significant shift in what we’re about to see in our feed and the overall experience on X. - The shift from reply guy tactics to authenticity is where it’s all headed. - The value of a post will not be defined by engagement in its entirety but by the native value of the content. Current State: - Content visibility, including posts and replies, is currently unevenly prioritized, with manually written heuristics favoring recommendations to followers, benefitting large accounts but limiting the reach of smaller ones. - The "For You" algo heavily relies on these heuristics - impacting content reach and potentially hindering smaller accounts, regardless of content quality. Proposed Changes: - Algo defined value assignment to content will be driven by End-to-End Architecture based AI. - User-specific vectors, representing preferences, will guide content prioritization, providing a refreshed user experience within the feed that is inclusive and personalized. - The new approach will promote quality content across the entire user base, irrespective of account size. Additional Insights: - Elon emphasizes that AI will play a pivotal role in content importance assignment, suggesting a departure from the current manually-driven processes. - The algo will differentiate between content from accounts users follow (non-AI recommended) and those they don't (AI recommended), which should significantly improve the diversity of the content we’re exposed to. Implications: The proposed changes signify a fundamental transformation in how content is presented and recommended, with a move towards a more democratic and AI-driven system. Smaller accounts may benefit from increased visibility, creating a more level playing field based on content quality rather than follower count. Theories: - The mention of AI-differentiated recommendations for followed and non-followed content suggests a strategic effort to broaden user exposure to diverse content. - The shift towards an AI-powered recommendation system indicates a commitment to enhancing user experience by tailoring content suggestions based on individual preferences. What we’re looking at is a change that finally addresses the FOMO dynamic between accounts with reach and truly compelling content. It will be a major chance for new accounts, small accounts, and anyone really - to advance into the greater ecosphere they were left out of before. Goodbye, reply guy. Hello, value adding content. These conclusions are based on the recent interview of Elon Musk by Lex Fridman.

Adrian Dittmann

313,553 Aufrufe • vor 2 Jahren

Micron is one of the most UNDERVALUED stocks in the entire AI trade right now and everyone should be buying at these prices. (Save this). Jensen laid out the situation in one sentence, the supply chain is lined up, the HBM is lined up with the Grace Blackwell GPUs, the only problem is that demand is much greater than the overall capacity of the world. And Michael Dell said it before Jensen even finished that memory is the single biggest supply constraint in the entire AI buildout right now. Every HBM chip that Micron, SK Hynix, and Samsung produce consumes three times the silicon wafer area of standard DRAM. Nvidia's Rubin GPU requires 288GB of HBM per chip, a 260% increase over the H100 in just two generations. Every major hyperscaler has locked up contracts through 2026, and Micron has said publicly it can only fulfill about two-thirds of medium-term demand for some customers. And it's HBM production is sold out entirely for 2026 and HBM4 is also already sold out. The numbers tell the story, DDR4 spot prices surged roughly 15x in eight months. DRAM contract prices rose 90-95% in a single quarter, TrendForce called it "essentially unprecedented" in the history of the memory market. Micron has rallied roughly 68% year to date in 2026, and yet it still trades at a P/E of 37.6x against an industry average of 75.3x. The shortage does not resolve until new fabs come online, Micron's new factories are not producing until 2027 and 2028 at the earliest, and the memory shortage is forecast to run until at least 2027. Milk Road Pro has been covering the HBM memory trade as a core AI infrastructure thesis before it became a consensus Wall Street call and our Pro members are already up massively in $MU. Come join us at the link in bio/below to see our full portfolio and the names we're watching before the rest of the market catches on.

Micron is one of the most UNDERVALUED stocks in the entire AI trade right now and everyone should be buying at these prices. (Save this). Jensen laid out the situation in one sentence, the supply chain is lined up, the HBM is lined up with the Grace Blackwell GPUs, the only problem is that demand is much greater than the overall capacity of the world. And Michael Dell said it before Jensen even finished that memory is the single biggest supply constraint in the entire AI buildout right now. Every HBM chip that Micron, SK Hynix, and Samsung produce consumes three times the silicon wafer area of standard DRAM. Nvidia's Rubin GPU requires 288GB of HBM per chip, a 260% increase over the H100 in just two generations. Every major hyperscaler has locked up contracts through 2026, and Micron has said publicly it can only fulfill about two-thirds of medium-term demand for some customers. And it's HBM production is sold out entirely for 2026 and HBM4 is also already sold out. The numbers tell the story, DDR4 spot prices surged roughly 15x in eight months. DRAM contract prices rose 90-95% in a single quarter, TrendForce called it "essentially unprecedented" in the history of the memory market. Micron has rallied roughly 68% year to date in 2026, and yet it still trades at a P/E of 37.6x against an industry average of 75.3x. The shortage does not resolve until new fabs come online, Micron's new factories are not producing until 2027 and 2028 at the earliest, and the memory shortage is forecast to run until at least 2027. Milk Road Pro has been covering the HBM memory trade as a core AI infrastructure thesis before it became a consensus Wall Street call and our Pro members are already up massively in $MU. Come join us at the link in bio/below to see our full portfolio and the names we're watching before the rest of the market catches on.

Milk Road AI

146,832 Aufrufe • vor 2 Monaten

Career update: i'm joining supermemory to lead Dev GTM. supermemory is the memory layer for AI products, we got the best public results on AI memory by a wide margin, 95% on the leading benchmarks. my role will be to make it easy for everyone to use. demos, walkthroughs, educational content, and the cleanest possible path from "i need memory in my agent" to shipping. pumped to be working with the coolest team oh, and the team just shipped supermemory local. fully self-contained, open source, runs on any machine in a minute!!

Career update: i'm joining supermemory to lead Dev GTM. supermemory is the memory layer for AI products, we got the best public results on AI memory by a wide margin, 95% on the leading benchmarks. my role will be to make it easy for everyone to use. demos, walkthroughs, educational content, and the cleanest possible path from "i need memory in my agent" to shipping. pumped to be working with the coolest team oh, and the team just shipped supermemory local. fully self-contained, open source, runs on any machine in a minute!!

Muskan Jain

111,830 Aufrufe • vor 1 Monat

🎥WATCH: SK GROUP CHAIRMAN DISCUSSES PLANS TO EXPAND U.S. INVESTMENT “My plan is much, much bigger.. far LARGER than $35 billion” He noted that the conglomerate has already committed over $35 billion in the U.S. across batteries and semiconductor projects. He adds that SK Hynix is exploring a shift toward becoming a “memory service provider,” where customers could lease memory capacity rather than simply buying physical chips.

🎥WATCH: SK GROUP CHAIRMAN DISCUSSES PLANS TO EXPAND U.S. INVESTMENT “My plan is much, much bigger.. far LARGER than $35 billion” He noted that the conglomerate has already committed over $35 billion in the U.S. across batteries and semiconductor projects. He adds that SK Hynix is exploring a shift toward becoming a “memory service provider,” where customers could lease memory capacity rather than simply buying physical chips.

Coin Bureau

31,685 Aufrufe • vor 13 Tagen

Appreciate you Charles for having me on today! We covered: • $AMD emerging as the second source of the AI economy • My two favorite emerging market names for 2026: $MELI & $JMIA • $MU at the center of the next AI spend cycle as AI becomes memory-first • $NVDA laying out the blueprint for the operating system of the AI economy • $META & $AMZN winning in AI through distribution, data, and workflow control • $EOSE as a winner from AI data centers creating extreme load swings that long-duration zinc storage is built to handle

Appreciate you Charles for having me on today! We covered: • $AMD emerging as the second source of the AI economy • My two favorite emerging market names for 2026: $MELI & $JMIA • $MU at the center of the next AI spend cycle as AI becomes memory-first • $NVDA laying out the blueprint for the operating system of the AI economy • $META & $AMZN winning in AI through distribution, data, and workflow control • $EOSE as a winner from AI data centers creating extreme load swings that long-duration zinc storage is built to handle

Shay Boloor

250,124 Aufrufe • vor 6 Monaten

THE AI BUBBLE MAY HAVE JUST ENTERED ITS MOST DANGEROUS PHASE. Trading is now live for SK Hynix, the company behind 56% of the world's HBM AI memory chips. It just completed the largest foreign listing in US history, raising $26.5 billion. The last company to hold this record was Alibaba in 2014. The S&P 500 corrected 15% soon after. History doesn't repeat exactly but it often rhymes.

THE AI BUBBLE MAY HAVE JUST ENTERED ITS MOST DANGEROUS PHASE. Trading is now live for SK Hynix, the company behind 56% of the world's HBM AI memory chips. It just completed the largest foreign listing in US history, raising $26.5 billion. The last company to hold this record was Alibaba in 2014. The S&P 500 corrected 15% soon after. History doesn't repeat exactly but it often rhymes.

Crypto Rover

69,944 Aufrufe • vor 14 Tagen

$MU at $1 trillion market cap. Meta at $1.3-1.4 trillion. Aravind Srinivas says Micron closes that gap in 6-12 months if HBM stays the binding constraint. On The Twenty Minute VC: every inference run needs HBM memory. That has pushed HBM costs up 5x in cost of goods. The entity that makes HBM gets the same structural advantage as the entity that makes the GPU - but without the GPU narrative already priced in. Srinivas's bottleneck framework: whatever is the hardest-to-replace constraint in the AI stack commands the price premium. Today that is HBM. CPUs are also getting scarce again because agent orchestration loops run on CPU, not GPU. The trade is the bottleneck, not the chip. Full infrastructure bottleneck breakdown: Source: 20VC with Harry Stebbings -

$MU at $1 trillion market cap. Meta at $1.3-1.4 trillion. Aravind Srinivas says Micron closes that gap in 6-12 months if HBM stays the binding constraint. On The Twenty Minute VC: every inference run needs HBM memory. That has pushed HBM costs up 5x in cost of goods. The entity that makes HBM gets the same structural advantage as the entity that makes the GPU - but without the GPU narrative already priced in. Srinivas's bottleneck framework: whatever is the hardest-to-replace constraint in the AI stack commands the price premium. Today that is HBM. CPUs are also getting scarce again because agent orchestration loops run on CPU, not GPU. The trade is the bottleneck, not the chip. Full infrastructure bottleneck breakdown: Source: 20VC with Harry Stebbings -

Podcast Alpha

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