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$MU customers are basically telling Micron that memory is no longer something they can casually buy later because AI has turned it into a strategic resource that needs to be secured years in advance. When customers put down deposits, sign multi-year agreements and accept minimum pricing then they're taking...

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Shay Boloor

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147,744 просмотров • 1 месяц назад •via X (Twitter)

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The most important part of $MU earnings was the strategic customer agreement structure Micron is building around the business. Micron has now signed 16 SCAs with 14 of them representing ~$100B of cumulative revenue at minimum contract pricing over the remaining term which shifts a huge portion of the business away from pure spot-market exposure and toward committed customer demand. Thats why the multiple discussion needs to change because a memory company with no visibility deserves a commodity multiple but a memory company with customer deposits, minimum pricing and long-term supply commitments deserves more credit for earnings durability.
1:07

The most important part of $MU earnings was the strategic customer agreement structure Micron is building around the business. Micron has now signed 16 SCAs with 14 of them representing ~$100B of cumulative revenue at minimum contract pricing over the remaining term which shifts a huge portion of the business away from pure spot-market exposure and toward committed customer demand. Thats why the multiple discussion needs to change because a memory company with no visibility deserves a commodity multiple but a memory company with customer deposits, minimum pricing and long-term supply commitments deserves more credit for earnings durability.

Shay Boloor

155,117 просмотров • 1 месяц назад

I still think $MU is cheap because the SCAs should force investors to rethink the multiple. Over time, Micron expects more than 50% of total revenue to be covered by these agreements giving the business a level of visibility memory companies historically never had. A memory company with no visibility deserves a commodity multiple but a memory company with minimum pricing, multi-year supply agreements and a structurally higher AI demand floor deserves more credit for normalized earnings power.
1:50

I still think $MU is cheap because the SCAs should force investors to rethink the multiple. Over time, Micron expects more than 50% of total revenue to be covered by these agreements giving the business a level of visibility memory companies historically never had. A memory company with no visibility deserves a commodity multiple but a memory company with minimum pricing, multi-year supply agreements and a structurally higher AI demand floor deserves more credit for normalized earnings power.

Shay Boloor

148,849 просмотров • 1 месяц назад

The selloff in Micron is one of the best buying opportunities you'll see this year (Save this). Sanjay Mehrotra just explained exactly why the old mental model for Micron, cyclical, commodity, mean reverting no longer applies. Every AI system, regardless of what device it runs on, requires more memory at higher performance to unlock its full potential. From data centers to smartphones to autonomous vehicles, memory is no longer a supporting actor but rather the critical bottleneck determining how fast AI can move. What makes this cycle structurally different starts with what happened in 2023. Certain customers drove industry pricing to one third of 2022 levels, forcing Micron into severe losses while still requiring $10 billion in investment just to stay competitive. Most companies in that situation cut spending and survive but Micron invested through the pain with the vision that the other side would be worth it. Those 2023 investments are now producing 84.9% gross margins, $41.46 billion in quarterly revenue, and Q4 guidance of $50 billion up from $11.3 billion in the same quarter just one year ago. That is what it looks like when a company bets on itself at exactly the right moment. Even Micron's own largest customers, Nvidia, Google, Amazon could not forecast the scale of AI memory demand that materialized. When the biggest technology companies in the world cannot project their own memory requirements, you are watching a structural transformation that nobody had models to predict, still in its early innings. Supply cannot respond quickly enough to close that gap. Mehrotra confirmed on air that tightness extends beyond 2027, new domestic fabs take years to bring online, and new HBM capacity which requires advanced 3D stacking that compounds in complexity at every generation won't meaningfully arrive until late 2028. There is no fast fix to a shortage of the most valuable memory on earth. The strategic customer agreements are the most underappreciated part of the entire story. Multi-year contracts with volume commitments and price floors now cover roughly 20% of DRAM volume and 30% of NAND volume, locking in a $100 billion contractual revenue base. The old Micron was at the mercy of customers who could crater prices overnight while the new Micron has contractual floors that make the 2023 scenario structurally impossible to repeat. Long Micron and make sure to follow me Melvin for more deep dives into AI and memory.
6:21

The selloff in Micron is one of the best buying opportunities you'll see this year (Save this). Sanjay Mehrotra just explained exactly why the old mental model for Micron, cyclical, commodity, mean reverting no longer applies. Every AI system, regardless of what device it runs on, requires more memory at higher performance to unlock its full potential. From data centers to smartphones to autonomous vehicles, memory is no longer a supporting actor but rather the critical bottleneck determining how fast AI can move. What makes this cycle structurally different starts with what happened in 2023. Certain customers drove industry pricing to one third of 2022 levels, forcing Micron into severe losses while still requiring $10 billion in investment just to stay competitive. Most companies in that situation cut spending and survive but Micron invested through the pain with the vision that the other side would be worth it. Those 2023 investments are now producing 84.9% gross margins, $41.46 billion in quarterly revenue, and Q4 guidance of $50 billion up from $11.3 billion in the same quarter just one year ago. That is what it looks like when a company bets on itself at exactly the right moment. Even Micron's own largest customers, Nvidia, Google, Amazon could not forecast the scale of AI memory demand that materialized. When the biggest technology companies in the world cannot project their own memory requirements, you are watching a structural transformation that nobody had models to predict, still in its early innings. Supply cannot respond quickly enough to close that gap. Mehrotra confirmed on air that tightness extends beyond 2027, new domestic fabs take years to bring online, and new HBM capacity which requires advanced 3D stacking that compounds in complexity at every generation won't meaningfully arrive until late 2028. There is no fast fix to a shortage of the most valuable memory on earth. The strategic customer agreements are the most underappreciated part of the entire story. Multi-year contracts with volume commitments and price floors now cover roughly 20% of DRAM volume and 30% of NAND volume, locking in a $100 billion contractual revenue base. The old Micron was at the mercy of customers who could crater prices overnight while the new Micron has contractual floors that make the 2023 scenario structurally impossible to repeat. Long Micron and make sure to follow me Melvin for more deep dives into AI and memory.

Melvin

130,037 просмотров • 25 дней назад

Jensen Huang just made a statement that every investor in AI infrastructure needs to hear (Save this). He said that the AI buildout is accelerating, the second half of this year is going to be much larger than the first half, and next year is going to be very, very large. Micron is the best positioned to win from this because every Nvidia GPU requires High Bandwidth Memory stacked directly on the chip to feed it data fast enough to keep up. There is no AI compute without memory, and right now there is simply not enough memory to go around. Micron's entire HBM supply for 2026 is already completely sold out under multi-year agreements before the year even started. Micron's own management has acknowledged they can only satisfy 50 to 65 percent of demand from some of their most important customers. That is not a problem that gets fixed quickly, because new fabs take years to build. Micron's Idaho expansion does not come online until mid-2026, a second Idaho facility is not expected until 2028, and a new New York fab is looking at 2030. The demand Jensen just described is arriving right now, and the supply to meet it is years away. The financial results already reflect this dynamic. Micron's Q2 fiscal 2026 revenue came in at $23.86 billion, nearly triple what it was a year earlier beating consensus by roughly $3.8 billion. The HBM market alone is expected to grow from $35 billion today to $100 billion by 2028, and Micron has been consistently ahead of that forecast. Jensen just told the world the second half of this year and all of next year are going to be larger than anything that came before. Micron is the company that supplies the memory those GPUs need to run, and it cannot build supply fast enough to keep up with demand. Come join Milk Road Pro for our full deep dive on Micron, the HBM supply thesis and our AI trade thesis! Link below!
1:28

Jensen Huang just made a statement that every investor in AI infrastructure needs to hear (Save this). He said that the AI buildout is accelerating, the second half of this year is going to be much larger than the first half, and next year is going to be very, very large. Micron is the best positioned to win from this because every Nvidia GPU requires High Bandwidth Memory stacked directly on the chip to feed it data fast enough to keep up. There is no AI compute without memory, and right now there is simply not enough memory to go around. Micron's entire HBM supply for 2026 is already completely sold out under multi-year agreements before the year even started. Micron's own management has acknowledged they can only satisfy 50 to 65 percent of demand from some of their most important customers. That is not a problem that gets fixed quickly, because new fabs take years to build. Micron's Idaho expansion does not come online until mid-2026, a second Idaho facility is not expected until 2028, and a new New York fab is looking at 2030. The demand Jensen just described is arriving right now, and the supply to meet it is years away. The financial results already reflect this dynamic. Micron's Q2 fiscal 2026 revenue came in at $23.86 billion, nearly triple what it was a year earlier beating consensus by roughly $3.8 billion. The HBM market alone is expected to grow from $35 billion today to $100 billion by 2028, and Micron has been consistently ahead of that forecast. Jensen just told the world the second half of this year and all of next year are going to be larger than anything that came before. Micron is the company that supplies the memory those GPUs need to run, and it cannot build supply fast enough to keep up with demand. Come join Milk Road Pro for our full deep dive on Micron, the HBM supply thesis and our AI trade thesis! Link below!

Milk Road AI

77,554 просмотров • 1 месяц назад

Micron is going to $4,000 and here is why (Save this). For 25 years, DRAM prices did one thing, they went down. Memory makers overbuilt, supply overwhelmed demand, buyers had all the negotiating leverage and that commodity trap crushed memory stocks every single cycle. What you are watching right now is a complete structural break from that 25 year trend. DRAM contract prices are up 700% year over year and the reason is AI and it is not going away. HBM3 was 12 layers, HBM4 in production and shipping now to Nvidia's latest GPUs is 16 layers. Each generation consumes significantly more wafer to produce than the last, meaning supply structurally tightens as the technology advances. Memory was 8% of hyperscaler capex in 2023 but is 35% in 2026 and is projected to hit 48% in 2027. Nearly half of everything Microsoft, Amazon, Google, and Meta spend on infrastructure will go to memory by next year. Going from the GB300 to the Vera Rubin 200 generation, GPU cost went up 57% while memory cost went up 435%. There are three companies on earth that can make DRAM at scale, Samsung, SK Hynix, and Micron. Both Samsung and SK Hynix are converting capacity to HBM which means conventional DRAM supply tightens further for everything else, and Micron captures pricing on both sides. Micron guided to $33.5 billion for Q3 and they reported $41.46 billion, a $7.96 billion beat, the largest earnings beat in the company's history. Gross margins came in at 85% above the 81% they guided. For Q4, they are now guiding to $50 billion in revenue with ~86% gross margins and $31 EPS. At $112 EPS in FY2027, the pre-earnings consensus and a 35x multiple, that is a $3,920 stock but with Q4 guiding to $31 EPS alone in a single quarter, FY2027 estimates will be revised meaningfully higher. Deutsche Bank says the supply-demand gap worsens through all of 2027 and into 2028. The market still thinks this is a cyclical bounce but this is far from it. This is the first chapters of a multi year repricing of the most critical component in the AI economy and Micron is at the center of it. Follow me Melvin for more AI, semis, and the next big market themes.
4:08

Micron is going to $4,000 and here is why (Save this). For 25 years, DRAM prices did one thing, they went down. Memory makers overbuilt, supply overwhelmed demand, buyers had all the negotiating leverage and that commodity trap crushed memory stocks every single cycle. What you are watching right now is a complete structural break from that 25 year trend. DRAM contract prices are up 700% year over year and the reason is AI and it is not going away. HBM3 was 12 layers, HBM4 in production and shipping now to Nvidia's latest GPUs is 16 layers. Each generation consumes significantly more wafer to produce than the last, meaning supply structurally tightens as the technology advances. Memory was 8% of hyperscaler capex in 2023 but is 35% in 2026 and is projected to hit 48% in 2027. Nearly half of everything Microsoft, Amazon, Google, and Meta spend on infrastructure will go to memory by next year. Going from the GB300 to the Vera Rubin 200 generation, GPU cost went up 57% while memory cost went up 435%. There are three companies on earth that can make DRAM at scale, Samsung, SK Hynix, and Micron. Both Samsung and SK Hynix are converting capacity to HBM which means conventional DRAM supply tightens further for everything else, and Micron captures pricing on both sides. Micron guided to $33.5 billion for Q3 and they reported $41.46 billion, a $7.96 billion beat, the largest earnings beat in the company's history. Gross margins came in at 85% above the 81% they guided. For Q4, they are now guiding to $50 billion in revenue with ~86% gross margins and $31 EPS. At $112 EPS in FY2027, the pre-earnings consensus and a 35x multiple, that is a $3,920 stock but with Q4 guiding to $31 EPS alone in a single quarter, FY2027 estimates will be revised meaningfully higher. Deutsche Bank says the supply-demand gap worsens through all of 2027 and into 2028. The market still thinks this is a cyclical bounce but this is far from it. This is the first chapters of a multi year repricing of the most critical component in the AI economy and Micron is at the center of it. Follow me Melvin for more AI, semis, and the next big market themes.

Melvin

92,821 просмотров • 1 месяц назад

Micron CEO just revealed one of the biggest investment themes of this decade. Semiconductors used to be a cyclical industry. Not anymore. His view is that Agentic AI is creating virtually bottomless demand. Combined with autonomous vehicles and humanoid robots, memory is becoming a strategic resource, almost like oil. He believes supply won’t be able to catch up with demand before 2027 to 2028. Until then, memory makers will remain in the driver’s seat.
1:53

Micron CEO just revealed one of the biggest investment themes of this decade. Semiconductors used to be a cyclical industry. Not anymore. His view is that Agentic AI is creating virtually bottomless demand. Combined with autonomous vehicles and humanoid robots, memory is becoming a strategic resource, almost like oil. He believes supply won’t be able to catch up with demand before 2027 to 2028. Until then, memory makers will remain in the driver’s seat.

Marcos Crypto

47,172 просмотров • 16 дней назад

MICRON $MU CEO SAYS THE COMPANY CAN ONLY SUPPLY 50-65% OF WHAT CUSTOMERS NEED RIGHT NOW This isn’t just Micron, Samsung, or SK Hynix. It’s a full-stack constraint that benefits the broader memory and storage ecosystem, including NAND from $SNDK and $WDC, capacity-driven disks from $STX, and system-level storage platforms and arrays from $P. AI demand for DRAM is outpacing supply by an “unprecedented” gap. New fabs take years, and the shortage is expected to persist beyond 2026.
1:26

MICRON $MU CEO SAYS THE COMPANY CAN ONLY SUPPLY 50-65% OF WHAT CUSTOMERS NEED RIGHT NOW This isn’t just Micron, Samsung, or SK Hynix. It’s a full-stack constraint that benefits the broader memory and storage ecosystem, including NAND from $SNDK and $WDC, capacity-driven disks from $STX, and system-level storage platforms and arrays from $P. AI demand for DRAM is outpacing supply by an “unprecedented” gap. New fabs take years, and the shortage is expected to persist beyond 2026.

Sam Badawi

385,928 просмотров • 2 месяцев назад

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
0:39

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,522 просмотров • 26 дней назад

Micron will be a $4,000 stock and here is why (Save this). As Sanjay Mehrotra puts it "We are only in the early innings of the significant innovation and productivity that can be unleashed in every part of the global economy over time." That is a roadmap and the math behind it points toward something most investors still haven't fully priced in. Q4 revenue guidance came in at $50 billion nearly $7 billion above what Wall Street was expecting for a single quarter. That number alone would have been Micron's entire annual revenue just two years ago and yet data centers are only the first chapter of this story. Mehrotra specifically called out robotics, humanoids, and fully autonomous vehicles as the next demand wave on tonight's call and the numbers behind those markets are genuinely staggering. A Level 4 autonomous vehicle requires over 300GB of DRAM, nearly 20 times more than a standard car today in every single vehicle that rolls off the assembly line. A humanoid robot will require between 64 and 128GB of DRAM and up to 2TB of NAND storage per unit, giving each one a memory footprint comparable to a high end server. There are projections for tens of millions of humanoid robots and hundreds of millions of autonomous vehicles over the next decade, and every single one of them runs on Micron memory. Mehrotra said directly tonight that tight supply conditions are expected to persist beyond calendar 2027 and even with aggressive capital spending Micron can only meet 50 to 67% of medium-term customer demand. Competitors new capacity will not meaningfully come online until late 2027 at the earliest, meaning the pricing environment Micron operates in today has years left to run. There was also 16 Strategic Customer Agreements with $100 billion in minimum committed revenue and $22 billion in customer cash deposits already paid, mean that even when the cycle eventually softens, Micron's floor has been permanently reset at a level no prior version of this company ever reached. Gross margins contractually protected above any peak in the company's prior history, locked through 2030, across half or more of total revenue and that is not a commodity business anymore. Bullish on memory. Follow Melvin for more AI, semis, and the next big market themes.
0:55

Micron will be a $4,000 stock and here is why (Save this). As Sanjay Mehrotra puts it "We are only in the early innings of the significant innovation and productivity that can be unleashed in every part of the global economy over time." That is a roadmap and the math behind it points toward something most investors still haven't fully priced in. Q4 revenue guidance came in at $50 billion nearly $7 billion above what Wall Street was expecting for a single quarter. That number alone would have been Micron's entire annual revenue just two years ago and yet data centers are only the first chapter of this story. Mehrotra specifically called out robotics, humanoids, and fully autonomous vehicles as the next demand wave on tonight's call and the numbers behind those markets are genuinely staggering. A Level 4 autonomous vehicle requires over 300GB of DRAM, nearly 20 times more than a standard car today in every single vehicle that rolls off the assembly line. A humanoid robot will require between 64 and 128GB of DRAM and up to 2TB of NAND storage per unit, giving each one a memory footprint comparable to a high end server. There are projections for tens of millions of humanoid robots and hundreds of millions of autonomous vehicles over the next decade, and every single one of them runs on Micron memory. Mehrotra said directly tonight that tight supply conditions are expected to persist beyond calendar 2027 and even with aggressive capital spending Micron can only meet 50 to 67% of medium-term customer demand. Competitors new capacity will not meaningfully come online until late 2027 at the earliest, meaning the pricing environment Micron operates in today has years left to run. There was also 16 Strategic Customer Agreements with $100 billion in minimum committed revenue and $22 billion in customer cash deposits already paid, mean that even when the cycle eventually softens, Micron's floor has been permanently reset at a level no prior version of this company ever reached. Gross margins contractually protected above any peak in the company's prior history, locked through 2030, across half or more of total revenue and that is not a commodity business anymore. Bullish on memory. Follow Melvin for more AI, semis, and the next big market themes.

Melvin

35,969 просмотров • 1 месяц назад

Micron is going to be a $4,000 stock and the CEO just told you exactly why in one interview (Save this). Micron is no longer a chip company but rather a America's monopoly on the most strategically critical material in the AI buildout. It's the only western company manufacturing memory at advanced nodes, sitting on $200 billion in committed domestic capex, with every unit of its highest value product already sold. let's start with the supply reality, Mehrotra said Micron can currently meet only 50% to two thirds of the demand from its key customers. That shortage will last well beyond 2027, and meaningful new supply from anyone in the industry does not arrive until 2028 at the earliest. Two more years of demand outpacing supply in a market growing 168% year over year and that is the floor on the bull case. Now layer on what makes this cycle structurally different from every one before it. Micron is the only American memory manufacturer on earth, Samsung and SK Hynix are South Korean. In a world where AI infrastructure has become a declared national security priority where Commerce Secretary Lutnick and Trade Ambassador Greer personally showed up to a fab dedication in Manassas, Virginia being the only US memory company is not just a competitive advantage. It is a government backed structural monopoly on the most critical input to the US AI buildout, backed by $6.2 billion in CHIPS Act subsidies across Idaho, New York, and Virginia. The $200 billion buildout spans Manassas for DDR4 defense and industrial memory, Boise for leading-edge DRAM with first wafers out mid 2027, a second Boise HBM fab with first wafers by end of 2028, and the Syracuse megafab, the largest semiconductor facility in US history, breaking ground January 2026 with up to four fabs over time. Combined, these sites take Micron's domestic production from 10% of its total output today to 40% over the next decade, and create 90,000 jobs in the process. The business model transformation is the real story. Come join Milk Road Pro for our full breakdown, our complete Micron valuation model incorporating the $200 billion domestic buildout and our entire AI thesis. Link below.
10:05

Micron is going to be a $4,000 stock and the CEO just told you exactly why in one interview (Save this). Micron is no longer a chip company but rather a America's monopoly on the most strategically critical material in the AI buildout. It's the only western company manufacturing memory at advanced nodes, sitting on $200 billion in committed domestic capex, with every unit of its highest value product already sold. let's start with the supply reality, Mehrotra said Micron can currently meet only 50% to two thirds of the demand from its key customers. That shortage will last well beyond 2027, and meaningful new supply from anyone in the industry does not arrive until 2028 at the earliest. Two more years of demand outpacing supply in a market growing 168% year over year and that is the floor on the bull case. Now layer on what makes this cycle structurally different from every one before it. Micron is the only American memory manufacturer on earth, Samsung and SK Hynix are South Korean. In a world where AI infrastructure has become a declared national security priority where Commerce Secretary Lutnick and Trade Ambassador Greer personally showed up to a fab dedication in Manassas, Virginia being the only US memory company is not just a competitive advantage. It is a government backed structural monopoly on the most critical input to the US AI buildout, backed by $6.2 billion in CHIPS Act subsidies across Idaho, New York, and Virginia. The $200 billion buildout spans Manassas for DDR4 defense and industrial memory, Boise for leading-edge DRAM with first wafers out mid 2027, a second Boise HBM fab with first wafers by end of 2028, and the Syracuse megafab, the largest semiconductor facility in US history, breaking ground January 2026 with up to four fabs over time. Combined, these sites take Micron's domestic production from 10% of its total output today to 40% over the next decade, and create 90,000 jobs in the process. The business model transformation is the real story. Come join Milk Road Pro for our full breakdown, our complete Micron valuation model incorporating the $200 billion domestic buildout and our entire AI thesis. Link below.

Milk Road AI

231,528 просмотров • 1 месяц назад

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.
1:03

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 просмотров • 1 месяц назад

Micron will be a $3,000 stock within the next couple of years and Jensen Huang gave the most important reason why (Save this). Jensen was asked if memory cyclical is just another boom and bust like it's always been? He said three years ago he sat down with Micron CEO Sanjay Mehrotra and explained exactly what the future of memory demand would look like and it is playing out exactly as he described. He did the same with SK Hynix's Tony Chey years earlier, Jensen was pre aligning Micron's entire product roadmap to a vision he had already mapped out years in advance. Then he explained why this cycle is structurally different from every previous one. AI has crossed the threshold into agentic, productive capability and the way to think about AI agents is as digital workers. And the earnings confirm it. Micron reported Q2 fiscal 2026 revenue of $23.86 billion up 196% yearover-year, beating estimates by 24%. Gross margins hit 74.4%, more than doubling in a single year and Q3 guidance is where it gets truly staggering. Micron guided for Q3 revenue of $33.5 billion up over 200% year over year with gross margins expected to exceed 81% and EPS projected at $19.15 against a consensus of $12.05. Analyst consensus sees full year fiscal 2027 revenue reaching $88 billion. and yet the valuation still has not caught up. Micron's HBM TAM forecast has been pulled forward two years to $100 billion by 2028, its entire 2026 HBM supply is already sold out, and HBM4 is already sold out too while the stock still trades at a forward P/E of just roughly 8x on FY2026 consensus earnings. The memory shortage is forecast to run until at least 2027 and new fabs don't come online until 2027 and 2028 at the earliest. 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 subscribers are already up massively in $MU Come join us using the link below to see our full portfolio and the names we're watching before the rest of the market catches on.
2:20

Micron will be a $3,000 stock within the next couple of years and Jensen Huang gave the most important reason why (Save this). Jensen was asked if memory cyclical is just another boom and bust like it's always been? He said three years ago he sat down with Micron CEO Sanjay Mehrotra and explained exactly what the future of memory demand would look like and it is playing out exactly as he described. He did the same with SK Hynix's Tony Chey years earlier, Jensen was pre aligning Micron's entire product roadmap to a vision he had already mapped out years in advance. Then he explained why this cycle is structurally different from every previous one. AI has crossed the threshold into agentic, productive capability and the way to think about AI agents is as digital workers. And the earnings confirm it. Micron reported Q2 fiscal 2026 revenue of $23.86 billion up 196% yearover-year, beating estimates by 24%. Gross margins hit 74.4%, more than doubling in a single year and Q3 guidance is where it gets truly staggering. Micron guided for Q3 revenue of $33.5 billion up over 200% year over year with gross margins expected to exceed 81% and EPS projected at $19.15 against a consensus of $12.05. Analyst consensus sees full year fiscal 2027 revenue reaching $88 billion. and yet the valuation still has not caught up. Micron's HBM TAM forecast has been pulled forward two years to $100 billion by 2028, its entire 2026 HBM supply is already sold out, and HBM4 is already sold out too while the stock still trades at a forward P/E of just roughly 8x on FY2026 consensus earnings. The memory shortage is forecast to run until at least 2027 and new fabs don't come online until 2027 and 2028 at the earliest. 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 subscribers are already up massively in $MU Come join us using the link below to see our full portfolio and the names we're watching before the rest of the market catches on.

Milk Road AI

87,425 просмотров • 1 месяц назад

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!
0:42

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

260,701 просмотров • 28 дней назад

Micron will be a $2,000 stock within the next couple of years and Jensen Huang just gave the most important reason why (Save this). Jensen was asked if memory cyclical is just another boom and bust like it's always been? Jensen's answer was remarkable. He said three years ago he sat down with Micron CEO Sanjay Mehrotra and explained exactly what the future of memory demand would look like and it is playing out exactly as he described. He did the same with SK Hynix's Tony Chey years earlier, Jensen was pre aligning Micron's entire product roadmap to a vision he had already mapped out years in advance. Then he explained why this cycle is structurally different from every previous one. AI has crossed the threshold into agentic, productive capability and the way to think about AI agents is as digital workers. Michael Dell put it plainly, you might soon have hundreds or thousands of digital agents working for you simultaneously, and each one requires compute, memory, storage, and networking at scale. The earnings confirm it. Micron reported Q2 fiscal 2026 revenue of $23.86 billion up 196% yearover-year, beating estimates by 24%. Gross margins hit 74.4%, more than doubling in a single year and Q3 guidance is where it gets truly staggering. Micron guided for Q3 revenue of $33.5 billion up over 200% year over year with gross margins expected to exceed 81% and EPS projected at $19.15 against a consensus of $12.05. Analyst consensus sees full-year fiscal 2027 revenue reaching $88 billion. and yet the valuation still has not caught up. Micron's HBM TAM forecast has been pulled forward two years to $100 billion by 2028, its entire 2026 HBM supply is already sold out, and HBM4 is already sold out too while the stock still trades at a forward P/E of just roughly 8x on FY2026 consensus earnings. The memory shortage is forecast to run until at least 2027 and new fabs don't come online until 2027 and 2028 at the earliest. 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 subscribers are already positioned 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.
2:20

Micron will be a $2,000 stock within the next couple of years and Jensen Huang just gave the most important reason why (Save this). Jensen was asked if memory cyclical is just another boom and bust like it's always been? Jensen's answer was remarkable. He said three years ago he sat down with Micron CEO Sanjay Mehrotra and explained exactly what the future of memory demand would look like and it is playing out exactly as he described. He did the same with SK Hynix's Tony Chey years earlier, Jensen was pre aligning Micron's entire product roadmap to a vision he had already mapped out years in advance. Then he explained why this cycle is structurally different from every previous one. AI has crossed the threshold into agentic, productive capability and the way to think about AI agents is as digital workers. Michael Dell put it plainly, you might soon have hundreds or thousands of digital agents working for you simultaneously, and each one requires compute, memory, storage, and networking at scale. The earnings confirm it. Micron reported Q2 fiscal 2026 revenue of $23.86 billion up 196% yearover-year, beating estimates by 24%. Gross margins hit 74.4%, more than doubling in a single year and Q3 guidance is where it gets truly staggering. Micron guided for Q3 revenue of $33.5 billion up over 200% year over year with gross margins expected to exceed 81% and EPS projected at $19.15 against a consensus of $12.05. Analyst consensus sees full-year fiscal 2027 revenue reaching $88 billion. and yet the valuation still has not caught up. Micron's HBM TAM forecast has been pulled forward two years to $100 billion by 2028, its entire 2026 HBM supply is already sold out, and HBM4 is already sold out too while the stock still trades at a forward P/E of just roughly 8x on FY2026 consensus earnings. The memory shortage is forecast to run until at least 2027 and new fabs don't come online until 2027 and 2028 at the earliest. 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 subscribers are already positioned 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

100,736 просмотров • 2 месяцев назад

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."
2:51

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."

MTS

35,125 просмотров • 1 месяц назад

Micron is going to $4,000 and this exactly why (Save this). Hyperscaler Capex, the combined spending of Amazon, Google, Meta, Microsoft and Oracle was $261 billion in 2024 and it hit $449 billion in 2025. Morgan Stanley now expects $805 billion in 2026 and $1.1 trillion in 2027. Memory consistently runs at 35–48% of that total hardware spend, apply that range to Morgan Stanley's numbers and you get somewhere between $280 billion and $530 billion flowing into memory stocks over the next two years alone. That is the market Micron is selling into right now and the company just reported $41.5 billion in revenue in a single quarter with 85% gross margins. But data centers are only the first wave. L2+ vehicles, cars with meaningful driver assistance carry over five times the memory of a standard car and that mix is doubling to over 20% of all vehicles sold this year, and Micron expects it to hit 40% by 2030. Autonomous vehicles will require over 300 gigabytes of DRAM per car, an 18x increase in memory content per unit, applied across tens of millions of cars a year. The third wave is the one that makes automotive look small, humanoid robots. A humanoid robot carries 10 times the memory of an average L2 vehicle. Tesla, Figure, and a growing list of US robotics companies are still in the very early stages while China is already scaling humanoid production fast. When the US robotics boom arrives and it will, the memory requirement per unit is orders of magnitude larger than anything that has come before. The one risk worth naming is if the hyperscalers signal a pause in spending in 2027, that puts real pressure on the thesis. But Morgan Stanley has raised their capex forecast by $630 billion in six months alone. The data center boom is already here,the car boom is arriving and the robotics boom hasn't started yet. Micron is the only US-based company that can supply all three. Follow me Melvin for more AI, semis and the next big market themes.
3:25

Micron is going to $4,000 and this exactly why (Save this). Hyperscaler Capex, the combined spending of Amazon, Google, Meta, Microsoft and Oracle was $261 billion in 2024 and it hit $449 billion in 2025. Morgan Stanley now expects $805 billion in 2026 and $1.1 trillion in 2027. Memory consistently runs at 35–48% of that total hardware spend, apply that range to Morgan Stanley's numbers and you get somewhere between $280 billion and $530 billion flowing into memory stocks over the next two years alone. That is the market Micron is selling into right now and the company just reported $41.5 billion in revenue in a single quarter with 85% gross margins. But data centers are only the first wave. L2+ vehicles, cars with meaningful driver assistance carry over five times the memory of a standard car and that mix is doubling to over 20% of all vehicles sold this year, and Micron expects it to hit 40% by 2030. Autonomous vehicles will require over 300 gigabytes of DRAM per car, an 18x increase in memory content per unit, applied across tens of millions of cars a year. The third wave is the one that makes automotive look small, humanoid robots. A humanoid robot carries 10 times the memory of an average L2 vehicle. Tesla, Figure, and a growing list of US robotics companies are still in the very early stages while China is already scaling humanoid production fast. When the US robotics boom arrives and it will, the memory requirement per unit is orders of magnitude larger than anything that has come before. The one risk worth naming is if the hyperscalers signal a pause in spending in 2027, that puts real pressure on the thesis. But Morgan Stanley has raised their capex forecast by $630 billion in six months alone. The data center boom is already here,the car boom is arriving and the robotics boom hasn't started yet. Micron is the only US-based company that can supply all three. Follow me Melvin for more AI, semis and the next big market themes.

Melvin

27,296 просмотров • 1 месяц назад

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!
3:30

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 просмотров • 1 месяц назад

AI companies just BROKE the global supply chain for every piece of technology you own. And the fallout is way worse than anyone predicted... Sony is delaying the next PlayStation to 2028 or 2029. Nintendo is hiking the Switch 2 price mid-cycle. Apple warned investors that iPhone margins are getting crushed. Cisco just posted its worst share loss in 4 years. Oppo is cutting phone shipments by 20%. Lenovo, Dell, HP, Acer, and ASUS are all raising laptop prices 15-20%. Samsung is now reviewing memory contracts QUARTERLY instead of annually because prices change too fast to plan. And Elon Musk just told investors Tesla has to build its own chip factory from scratch because no supplier on the planet can keep up. His exact words: "We've got two choices: hit the chip wall or make a fab." All of this happened in the last 3 weeks. Same cause. Every single time. AI data centers are buying every memory chip on Earth. And there's nothing left for everyone else. Here's how we got here: 3 years ago, ChatGPT launched and the AI arms race began. Since then, Samsung, SK Hynix, and Micron, the only 3 companies that make memory chips, quietly made a decision that's now reshaping the ENTIRE global economy. They stopped prioritizing consumer memory. Every factory. Every production line. Every wafer. All redirected toward one customer: AI data centers Why? Money. AI memory chips sell for 3-5X the margin of regular RAM. When Google calls offering to buy your entire output at premium pricing, you don't say no. So the 3 companies that control 90% of the world's memory supply chose their highest-paying customers and left everyone else fighting over scraps. The numbers from this week are insane: OpenAI's Stargate project ALONE will consume 40% of the entire world's DRAM output. HBM demand is surging 70% year over year in 2026. HBM now takes 23% of total DRAM wafer production, up from 19% last year. Meanwhile, there's a 4% gap between global DRAM supply and demand. And that doesn't even account for depleted inventories across multiple industries. DRAM prices have surged over 170% since early 2025. DDR5 contract prices are still jumping double digits month over month. And the memory makers? They're printing money. Micron's revenue is expected to more than DOUBLE this fiscal year. SK Hynix sales doubled in 2024 and are on pace to double AGAIN. Samsung just reported quarterly profit nearly tripling. 3 companies. $650 billion in AI spending chasing their products. And they get to name their price. But the collateral damage is everywhere: Every industry that uses memory, which is every industry, is getting squeezed. Smartphone manufacturers are getting destroyed. For a mid-range phone, memory now represents up to 30% of the total build cost. Triple what it was in early 2025. Chinese phone makers like Xiaomi, Oppo, and Transsion are cutting shipment forecasts and raising prices because they literally cannot afford the memory to build their phones. Lenovo's CFO called the cost surge "unprecedented" and admitted they stockpiled 50% more inventory than normal just to survive the next few months. The PC market could shrink by up to 9% this year according to IDC. Not because people don't want computers. But because they can't afford the memory that goes inside them. And the gaming industry? Sony is seriously considering pushing the next PlayStation to 2028 or 2029. Their carefully planned console cycle is getting blown up because they can't secure memory at prices that make a new console viable. Nintendo is looking at raising the Switch 2 price. In the middle of a launch cycle. Something console makers almost never do. Nvidia is cutting RTX GPU production because they can't get enough GDDR7 memory. Even the car industry is getting hit... Analysts are warning about a repeat of the pandemic-era chip shortage that shut down auto factories worldwide. All because AI companies decided their chatbots needed the memory more than your car does. And this doesn't get better for YEARS. Building a new memory fab takes 3-5 years minimum. Micron's new factory in Idaho won't meaningfully increase supply until 2027 at the earliest. By then, AI demand will have grown even more. Memory makers are already selling their 2027 AND 2028 capacity to AI customers today. There is no supply relief coming. That's why Elon is planning to build Tesla's own "TeraFab," a massive semiconductor plant that makes logic chips, memory, AND packaging all under one roof. He said existing suppliers including TSMC, Samsung, and Micron simply cannot supply Tesla at the levels the company needs. Think about that. One of the richest men in the world, running one of the largest companies on Earth, can't buy enough memory chips. So he's building his own factory. If ELON can't get supply, what chance does everyone else have? The AI revolution has a tax. And YOU'RE paying it. Every dollar Big Tech spends on AI infrastructure drives up the cost of the memory inside your phone, your laptop, your car, your TV, and your gaming console. $650 billion in AI spending this year. 3 companies controlling 90% of the memory supply. And every wafer they allocate to an Nvidia GPU is a wafer denied to the device in your pocket. The AI boom isn't free. You're subsidizing it every time you buy a piece of technology. And the bill just went up like crazy.
1:31

AI companies just BROKE the global supply chain for every piece of technology you own. And the fallout is way worse than anyone predicted... Sony is delaying the next PlayStation to 2028 or 2029. Nintendo is hiking the Switch 2 price mid-cycle. Apple warned investors that iPhone margins are getting crushed. Cisco just posted its worst share loss in 4 years. Oppo is cutting phone shipments by 20%. Lenovo, Dell, HP, Acer, and ASUS are all raising laptop prices 15-20%. Samsung is now reviewing memory contracts QUARTERLY instead of annually because prices change too fast to plan. And Elon Musk just told investors Tesla has to build its own chip factory from scratch because no supplier on the planet can keep up. His exact words: "We've got two choices: hit the chip wall or make a fab." All of this happened in the last 3 weeks. Same cause. Every single time. AI data centers are buying every memory chip on Earth. And there's nothing left for everyone else. Here's how we got here: 3 years ago, ChatGPT launched and the AI arms race began. Since then, Samsung, SK Hynix, and Micron, the only 3 companies that make memory chips, quietly made a decision that's now reshaping the ENTIRE global economy. They stopped prioritizing consumer memory. Every factory. Every production line. Every wafer. All redirected toward one customer: AI data centers Why? Money. AI memory chips sell for 3-5X the margin of regular RAM. When Google calls offering to buy your entire output at premium pricing, you don't say no. So the 3 companies that control 90% of the world's memory supply chose their highest-paying customers and left everyone else fighting over scraps. The numbers from this week are insane: OpenAI's Stargate project ALONE will consume 40% of the entire world's DRAM output. HBM demand is surging 70% year over year in 2026. HBM now takes 23% of total DRAM wafer production, up from 19% last year. Meanwhile, there's a 4% gap between global DRAM supply and demand. And that doesn't even account for depleted inventories across multiple industries. DRAM prices have surged over 170% since early 2025. DDR5 contract prices are still jumping double digits month over month. And the memory makers? They're printing money. Micron's revenue is expected to more than DOUBLE this fiscal year. SK Hynix sales doubled in 2024 and are on pace to double AGAIN. Samsung just reported quarterly profit nearly tripling. 3 companies. $650 billion in AI spending chasing their products. And they get to name their price. But the collateral damage is everywhere: Every industry that uses memory, which is every industry, is getting squeezed. Smartphone manufacturers are getting destroyed. For a mid-range phone, memory now represents up to 30% of the total build cost. Triple what it was in early 2025. Chinese phone makers like Xiaomi, Oppo, and Transsion are cutting shipment forecasts and raising prices because they literally cannot afford the memory to build their phones. Lenovo's CFO called the cost surge "unprecedented" and admitted they stockpiled 50% more inventory than normal just to survive the next few months. The PC market could shrink by up to 9% this year according to IDC. Not because people don't want computers. But because they can't afford the memory that goes inside them. And the gaming industry? Sony is seriously considering pushing the next PlayStation to 2028 or 2029. Their carefully planned console cycle is getting blown up because they can't secure memory at prices that make a new console viable. Nintendo is looking at raising the Switch 2 price. In the middle of a launch cycle. Something console makers almost never do. Nvidia is cutting RTX GPU production because they can't get enough GDDR7 memory. Even the car industry is getting hit... Analysts are warning about a repeat of the pandemic-era chip shortage that shut down auto factories worldwide. All because AI companies decided their chatbots needed the memory more than your car does. And this doesn't get better for YEARS. Building a new memory fab takes 3-5 years minimum. Micron's new factory in Idaho won't meaningfully increase supply until 2027 at the earliest. By then, AI demand will have grown even more. Memory makers are already selling their 2027 AND 2028 capacity to AI customers today. There is no supply relief coming. That's why Elon is planning to build Tesla's own "TeraFab," a massive semiconductor plant that makes logic chips, memory, AND packaging all under one roof. He said existing suppliers including TSMC, Samsung, and Micron simply cannot supply Tesla at the levels the company needs. Think about that. One of the richest men in the world, running one of the largest companies on Earth, can't buy enough memory chips. So he's building his own factory. If ELON can't get supply, what chance does everyone else have? The AI revolution has a tax. And YOU'RE paying it. Every dollar Big Tech spends on AI infrastructure drives up the cost of the memory inside your phone, your laptop, your car, your TV, and your gaming console. $650 billion in AI spending this year. 3 companies controlling 90% of the memory supply. And every wafer they allocate to an Nvidia GPU is a wafer denied to the device in your pocket. The AI boom isn't free. You're subsidizing it every time you buy a piece of technology. And the bill just went up like crazy.

Ricardo

567,416 просмотров • 5 месяцев назад

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

594,453 просмотров • 6 месяцев назад

Gavin Baker accurately predicted Micron's 10x run in 2025 Now he's making his next big call: "DRAM is probably going to be 30 to 40% of all hyperscaler CapEx next year, every hundreds of billions of dollars spent goes straight to DRAM" He calls it the single most important bottleneck in all of AI, above lasers, power chips, NAND or HDDs, Elon is even turning the TeraFab toward memory because he sees the same thing Only three firms on earth can make the memory these AI servers need "This is as close to magic as science can get" And Micron just locked in supply contracts covering half its revenue, with floor prices already above the margin peaks of past cycles, the economics of the stock are not what they were "If you still hold $MU at a commodity discount to $ASML and $LRCX, that discount is no longer earned" bookmark it, one of the strongest memory theses for 2026-2027 ↓
5:47

Gavin Baker accurately predicted Micron's 10x run in 2025 Now he's making his next big call: "DRAM is probably going to be 30 to 40% of all hyperscaler CapEx next year, every hundreds of billions of dollars spent goes straight to DRAM" He calls it the single most important bottleneck in all of AI, above lasers, power chips, NAND or HDDs, Elon is even turning the TeraFab toward memory because he sees the same thing Only three firms on earth can make the memory these AI servers need "This is as close to magic as science can get" And Micron just locked in supply contracts covering half its revenue, with floor prices already above the margin peaks of past cycles, the economics of the stock are not what they were "If you still hold $MU at a commodity discount to $ASML and $LRCX, that discount is no longer earned" bookmark it, one of the strongest memory theses for 2026-2027 ↓

Damir Akaza

817,530 просмотров • 28 дней назад