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Elon Musk says there isn’t a single high-volume computer memory fab operating in America today. “There’s one being built in Idaho by Micron, but it won’t reach volume production until around 2028.” “There are some being built in New York, but they’re expected in 2029 and 2030.” “Even under... show more

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50,604 views • 3 months ago •via X (Twitter)

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8 Comments

Bruce Martin3 months ago

memory is the new oil, except it’s harder to find and cheaper to lose sleep over

Leopold Stock Tracker3 months ago

True

MultifamilyRealEstate.com3 months ago

SanDisk and MICRON still have twice their current valuation to go, or more! SNDK is a $5,000 stock, they're going to split it 10:1 at $2,560 which will trigger and massive buying frenzy, and drive it up to $500, relatively fast! If you're not in now, you'll miss all of that!

Killer.🐻⛓️3 months ago

2028 at the earliest and demand doubles every year, fabs can't keep up. @SentientAGI routes workloads across distributed compute instead of waiting on silicon that doesn't exist yet. bottleneck shifts to coordination not hardware

Matt3 months ago

@grok what is the current market share of pure EV sales in the US thus far in 2026? Also, what year did the Model S launch?

Loepald Stock Tarkcer3 months ago

Details of my stock holdings are as follows ⬇️

Bengin | getting you in front of your ICP3 months ago

feels like memory is the real chips race now, not just compute if demand keeps compounding like this, 2028 already sounds late

Wes Eklund3 months ago

memory is the quiet choke point, and it’s about to get super loud Elon will probably start building these memory fabs

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$Elon Musk on Terafab: "It's worth noting that there's not a single high volume computer memory fab in America right now, zero. There's one being built by Micron, but that will not reach volume production until I believe 2028 and there's something built in New York, but they are in, I think, 2029 and 2030, and this is a tiny fraction of the memory that's needed, and in fact, even if you take the best case assumptions of the memory makers and the logic makers, it is not enough to meet the demand that is anticipated, which is why you're seeing stocks of like Micron go to, I think, 1.2 trillion, or some quite high number, so there's just clearly a need for AI logic memory and packaging, AI computers, essentially, that is far beyond what even the best case assumptions of the existing fabricators can do, and that's why we need to do the Terafab. It seems essential, otherwise we will not, there will not be enough chips."$

Elon Musk on Terafab: "It's worth noting that there's not a single high volume computer memory fab in America right now, zero. There's one being built by Micron, but that will not reach volume production until I believe 2028 and there's something built in New York, but they are in, I think, 2029 and 2030, and this is a tiny fraction of the memory that's needed, and in fact, even if you take the best case assumptions of the memory makers and the logic makers, it is not enough to meet the demand that is anticipated, which is why you're seeing stocks of like Micron go to, I think, 1.2 trillion, or some quite high number, so there's just clearly a need for AI logic memory and packaging, AI computers, essentially, that is far beyond what even the best case assumptions of the existing fabricators can do, and that's why we need to do the Terafab. It seems essential, otherwise we will not, there will not be enough chips."

DogeDesigner

148,233 views • 2 months ago

Elon Musk explains the biggest reason why Tesla and SpaceX is building a TerraFab: “What’s the limiting factor? We see the limiting factor as being able to make chips - both logic, memory and packaging” Today, the U.S. has zero high-volume computer memory fabs in production. New facilities are coming, but they won't reach meaningful scale until 2028–2030......far behind the pace AI demand is growing Without enough semiconductors, AI and robotics eventually hit a physical wall TerraFab is designed to attack that bottleneck directly...bringing logic, memory and advanced packaging under one roof

Elon Musk explains the biggest reason why Tesla and SpaceX is building a TerraFab: “What’s the limiting factor? We see the limiting factor as being able to make chips - both logic, memory and packaging” Today, the U.S. has zero high-volume computer memory fabs in production. New facilities are coming, but they won't reach meaningful scale until 2028–2030......far behind the pace AI demand is growing Without enough semiconductors, AI and robotics eventually hit a physical wall TerraFab is designed to attack that bottleneck directly...bringing logic, memory and advanced packaging under one roof

X Freeze

14,529 views • 2 months ago

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.

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 views • 2 months ago

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.

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

Ricardo

594,643 views • 8 months ago

People keep saying $MU can just build more fabs to solve the shortage but it doesn't work like that. AI demand is growing faster than the memory industry can add capacity, and new fabs cannot be built overnight. Micron’s first new Idaho fab will not begin producing wafers until mid-2027, its second Idaho fab and Japanese expansion are expected to start output in late 2028, and production at the New York site is not expected until 2030. Even after a fab opens, installing equipment, qualifying products and reaching full production take additional time. This means Micron cannot immediately satisfy every customer but neither can Samsung or SK Hynix. The entire industry is dealing with the same construction timelines and technical challenges, while HBM and advanced DRAM require more manufacturing capacity than traditional memory. Micron expects both DRAM and NAND to remain supply constrained through 2027 and 2028, with no clear timeline for when supply will catch up with demand. More than 75% of Micron’s 2027 output is already committed, and discussions are shifting toward 2028 capacity. Customers are now locking in supply years ahead of time because Micron has signed 26 strategic customer agreements covering an estimated 35% of its revenue through 2030. Those agreements include long term volume commitments, pricing frameworks and $32 billion of customer financial commitments, mostly through cash deposits. That completely changes the memory cycle because in the past, producers built too much capacity, supply exceeded demand and memory prices collapsed. Today, customers are reserving capacity in advance because they are more worried about securing enough memory than negotiating the lowest possible price. Micron does not need unlimited supply right now but rather needs controlled supply growth, strong pricing and customers willing to fund future capacity and that is exactly what is happening. Bullish on Micron and I’m still heavily positioned around the memory cycle, and if you want to see exactly how I’m positioned in $MU and the rest of the memory trade, check out my full portfolio below.

People keep saying $MU can just build more fabs to solve the shortage but it doesn't work like that. AI demand is growing faster than the memory industry can add capacity, and new fabs cannot be built overnight. Micron’s first new Idaho fab will not begin producing wafers until mid-2027, its second Idaho fab and Japanese expansion are expected to start output in late 2028, and production at the New York site is not expected until 2030. Even after a fab opens, installing equipment, qualifying products and reaching full production take additional time. This means Micron cannot immediately satisfy every customer but neither can Samsung or SK Hynix. The entire industry is dealing with the same construction timelines and technical challenges, while HBM and advanced DRAM require more manufacturing capacity than traditional memory. Micron expects both DRAM and NAND to remain supply constrained through 2027 and 2028, with no clear timeline for when supply will catch up with demand. More than 75% of Micron’s 2027 output is already committed, and discussions are shifting toward 2028 capacity. Customers are now locking in supply years ahead of time because Micron has signed 26 strategic customer agreements covering an estimated 35% of its revenue through 2030. Those agreements include long term volume commitments, pricing frameworks and $32 billion of customer financial commitments, mostly through cash deposits. That completely changes the memory cycle because in the past, producers built too much capacity, supply exceeded demand and memory prices collapsed. Today, customers are reserving capacity in advance because they are more worried about securing enough memory than negotiating the lowest possible price. Micron does not need unlimited supply right now but rather needs controlled supply growth, strong pricing and customers willing to fund future capacity and that is exactly what is happening. Bullish on Micron and I’m still heavily positioned around the memory cycle, and if you want to see exactly how I’m positioned in $MU and the rest of the memory trade, check out my full portfolio below.

Melvin

19,623 views • 5 days ago

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!

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 views • 3 months ago

Bank of America just upgraded $MU and memory saying that AI hardware infrastructure spending "is not slowing down." They also said that "the market is underestimating" Micron stock and note it as a "time to buy." Buy every major dip on memory and you will be rich in the future. 1. Sandisk $SNDK 2. Micron $MU All my buy and sell signals in Discord @

Bank of America just upgraded $MU and memory saying that AI hardware infrastructure spending "is not slowing down." They also said that "the market is underestimating" Micron stock and note it as a "time to buy." Buy every major dip on memory and you will be rich in the future. 1. Sandisk $SNDK 2. Micron $MU All my buy and sell signals in Discord @

StockWhale

69,319 views • 3 months ago

MICRON $MU CEO IS LITERALLY TELLING EVERYONE EXACTLY WHAT TO BUY: He says that memory supply will not catch up to demand until atleast 2028. Memory companies are priced at commodity multiples like the demand will stop this year or next year. That is not the case, and the Micron CEO is shouting it.

MICRON $MU CEO IS LITERALLY TELLING EVERYONE EXACTLY WHAT TO BUY: He says that memory supply will not catch up to demand until atleast 2028. Memory companies are priced at commodity multiples like the demand will stop this year or next year. That is not the case, and the Micron CEO is shouting it.

Just a Dude Who Invests

21,976 views • 1 month ago

Memory supply and demand won’t find parity until 2030 (or later) and with new AI powered innovation in and out of the datacenter the constraint will remain giving memory names asymmetric pricing power for a prolonged cycle that is still at its beginning.

Memory supply and demand won’t find parity until 2030 (or later) and with new AI powered innovation in and out of the datacenter the constraint will remain giving memory names asymmetric pricing power for a prolonged cycle that is still at its beginning.

Daniel Newman

28,092 views • 3 months ago

Elon Musk said Tesla will soon reveal the location and full plans for Terafab and the announcement will get its own dedicated spotlight: “Terafab is going to be an amazing initiative and a necessary one Without it, we will be constrained in our ability to scale Optimus production because we simply will not have enough AI chips We have to solve memory, logic and packaging in order to scale Optimus” Tesla has already placed equipment orders for a development fab in Austin that will bring: • Lithography mask production • Logic chips • Memory • Advanced packaging • Chip testing All under one roof This would allow Tesla to design a new AI chip, manufacture it, package it, test it and quickly iterate on the next version from the same facility A building like this does not exist anywhere else on Earth Terafab is not just another factory It is going to become the chip-development engine Tesla needs to scale Optimus and pursue high-risk, high-reward AI hardware breakthroughs at unprecedented speed

Elon Musk said Tesla will soon reveal the location and full plans for Terafab and the announcement will get its own dedicated spotlight: “Terafab is going to be an amazing initiative and a necessary one Without it, we will be constrained in our ability to scale Optimus production because we simply will not have enough AI chips We have to solve memory, logic and packaging in order to scale Optimus” Tesla has already placed equipment orders for a development fab in Austin that will bring: • Lithography mask production • Logic chips • Memory • Advanced packaging • Chip testing All under one roof This would allow Tesla to design a new AI chip, manufacture it, package it, test it and quickly iterate on the next version from the same facility A building like this does not exist anywhere else on Earth Terafab is not just another factory It is going to become the chip-development engine Tesla needs to scale Optimus and pursue high-risk, high-reward AI hardware breakthroughs at unprecedented speed

X Freeze

18,749 views • 2 months ago

Ben Thompson says the memory makers are Iran, and by making the Strait of Hormuz mistake are inviting the whole industry to build around them "The other thing, the risk they run, is Apple. Apple's lobbying to get Chinese memory, right?" "And what is the number one focus of algorithmic changes? How can we use less memory?" "I think the memory makers probably screw themselves in the long run by creating such a massive target on their back." "I've analogized memory makers to Iran. The issue with the Strait of Hormuz is it's very effective. It's more effective if you don't use it, cuz then it's always hanging out there as something you could do. Now they did it. Turns out it worked." "But the UAE and Saudi Arabia, they're going to build pipelines. They're going to build new ports. They're not going to let this happen again." "It's very painful right now. But say Iran wants to close the Strait of Hormuz in 2035. It's not going to have any effect, because it will have been built around." "My concern for the memory makers is they might have done the same thing. No one's going to let themselves get in this situation again as far as memory goes."

Ben Thompson says the memory makers are Iran, and by making the Strait of Hormuz mistake are inviting the whole industry to build around them "The other thing, the risk they run, is Apple. Apple's lobbying to get Chinese memory, right?" "And what is the number one focus of algorithmic changes? How can we use less memory?" "I think the memory makers probably screw themselves in the long run by creating such a massive target on their back." "I've analogized memory makers to Iran. The issue with the Strait of Hormuz is it's very effective. It's more effective if you don't use it, cuz then it's always hanging out there as something you could do. Now they did it. Turns out it worked." "But the UAE and Saudi Arabia, they're going to build pipelines. They're going to build new ports. They're not going to let this happen again." "It's very painful right now. But say Iran wants to close the Strait of Hormuz in 2035. It's not going to have any effect, because it will have been built around." "My concern for the memory makers is they might have done the same thing. No one's going to let themselves get in this situation again as far as memory goes."

Fireside Alpha

205,967 views • 1 month ago

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

260,701 views • 3 months ago

Ben Thompson uses the Strait of Hormuz to explain why memory makers squeezing the market now guarantees they lose their leverage later: "I think the memory makers probably screw themselves in the long run by creating such a massive target on their back. The issue with the Strait of Hormuz is that it's very effective if you don't use it. Then it's always hanging out there as something you could do. Now they did, and it turns out it worked. But the UAE and Saudi Arabia, they're going to build pipelines, they're going to build new ports. They're not going to let this happen again. So it's very painful right now, but say Iran wants to close the Strait of Hormuz in 2035. It's not going to have any effect, because it will have been built around. My concern for the memory makers is they might have done the same thing. No one is going to let themselves get in this situation again."

Ben Thompson uses the Strait of Hormuz to explain why memory makers squeezing the market now guarantees they lose their leverage later: "I think the memory makers probably screw themselves in the long run by creating such a massive target on their back. The issue with the Strait of Hormuz is that it's very effective if you don't use it. Then it's always hanging out there as something you could do. Now they did, and it turns out it worked. But the UAE and Saudi Arabia, they're going to build pipelines, they're going to build new ports. They're not going to let this happen again. So it's very painful right now, but say Iran wants to close the Strait of Hormuz in 2035. It's not going to have any effect, because it will have been built around. My concern for the memory makers is they might have done the same thing. No one is going to let themselves get in this situation again."

Invest Like the Best

21,582 views • 1 month ago

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 Gavin 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. This is exactly why we’re still tracking memory so closely at Milk Road. HBM is becoming less of a commodity and more of a critical custom component for AI, and we think that changes how this cycle should be valued. If you want to see what we’re actually buying and trading around this theme, come join us for just $1 using the link:

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 Gavin 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. This is exactly why we’re still tracking memory so closely at Milk Road. HBM is becoming less of a commodity and more of a critical custom component for AI, and we think that changes how this cycle should be valued. If you want to see what we’re actually buying and trading around this theme, come join us for just $1 using the link:

Milk Road AI

30,831 views • 1 month ago

$MU is the only company right now fully positioned for the memory centric AI cycle. This will, without question, be one of the largest markets over the next 5 years high bandwidth memory, advanced packaging, and AI server demand are structurally exploding. And the market still hasn’t fully priced this in. $DELL is simultaneously capturing the AI infrastructure build out from servers to enterprise deployment with multi year visibility and backlog strength that most investors are still underestimating. I’ll be going down a rabbit hole on both MU and DELL this long weekend.

$MU is the only company right now fully positioned for the memory centric AI cycle. This will, without question, be one of the largest markets over the next 5 years high bandwidth memory, advanced packaging, and AI server demand are structurally exploding. And the market still hasn’t fully priced this in. $DELL is simultaneously capturing the AI infrastructure build out from servers to enterprise deployment with multi year visibility and backlog strength that most investors are still underestimating. I’ll be going down a rabbit hole on both MU and DELL this long weekend.

Bill Brooklyn

23,185 views • 1 month ago

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.

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

131,779 views • 3 months ago

The Father of HBM, Kim Jung-ho says physical AI is really a memory problem, because a self-driving car can't react faster than its memory can recall what it learned, and the stakes are: it cannot be a second late on the brakes. "Physical AI is memory AI." "The car crashes as it drives, remembers it all, and learns from it." "What's harder is the learning and reasoning must be real-time. A self-driving car can't hit the brakes a second late." "So here not just capacity, but speed matters too. Memory's speed." "Future memory will lead the AI revolution, and there it's speed and capacity. Speed is bandwidth, capacity is stacking. That's how I see it." ______ More from his lecture here:

The Father of HBM, Kim Jung-ho says physical AI is really a memory problem, because a self-driving car can't react faster than its memory can recall what it learned, and the stakes are: it cannot be a second late on the brakes. "Physical AI is memory AI." "The car crashes as it drives, remembers it all, and learns from it." "What's harder is the learning and reasoning must be real-time. A self-driving car can't hit the brakes a second late." "So here not just capacity, but speed matters too. Memory's speed." "Future memory will lead the AI revolution, and there it's speed and capacity. Speed is bandwidth, capacity is stacking. That's how I see it." ______ More from his lecture here:

Fireside Alpha

80,580 views • 2 months ago

🚨 NOBODY IS PREPARED FOR WHAT $MU IS ABOUT TO DO NEXT Micron Technologies has been leading the memory sector in this AI rally. That was phase 1... Now we enter phase 2: the re-accumulation stage. But, phase 3 is where it gets fun. There is only 1 place to get memory in America and that's $MU. Therefore it will likely lead the race again. Do not miss this.

🚨 NOBODY IS PREPARED FOR WHAT $MU IS ABOUT TO DO NEXT Micron Technologies has been leading the memory sector in this AI rally. That was phase 1... Now we enter phase 2: the re-accumulation stage. But, phase 3 is where it gets fun. There is only 1 place to get memory in America and that's $MU. Therefore it will likely lead the race again. Do not miss this.

Coinvo Trading

40,772 views • 2 months ago

Memory isn’t just a feature in AI, it’s the difference between a system that responds and one that truly understands. In the first episode of The Long Walk, Sudarshan Kamath and I dive into how memory is shaping the next generation of voice agents, and why it’s becoming critical for real enterprise workflows.

Memory isn’t just a feature in AI, it’s the difference between a system that responds and one that truly understands. In the first episode of The Long Walk, Sudarshan Kamath and I dive into how memory is shaping the next generation of voice agents, and why it’s becoming critical for real enterprise workflows.

Nishkarsh

30,316 views • 7 months ago

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!

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

130,756 views • 3 months ago