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Dylan Patel says the memory shortage is not a normal cycle "Memory capacity is only growing 20, 30% a year for the next three years. And yet, demand is doubling, is doubling...memory prices are going to keep soaring." "Our point there was memory isn't a shortage, and this is...

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Dylan Patel says memory gross margins are headed to 90% and then at some point will head back down to the 70s or even lower. "Memory companies, they're in a commodity market." "They let the spot market and contract market sort of supply-demand balance really adjust pricing." "And so you see these two, three axes in pricing, and someday you'll see pricing halve." "Because memory necessarily doesn't deserve a margin of 85%, which is where it's headed to. We're still not at 85, 90% gross margins for memory, but we'll get there." "And then at some point from there, it'll also halve back down to like 70s or maybe even lower. And so we'll see this oscillation in memory."
0:29

Dylan Patel says memory gross margins are headed to 90% and then at some point will head back down to the 70s or even lower. "Memory companies, they're in a commodity market." "They let the spot market and contract market sort of supply-demand balance really adjust pricing." "And so you see these two, three axes in pricing, and someday you'll see pricing halve." "Because memory necessarily doesn't deserve a margin of 85%, which is where it's headed to. We're still not at 85, 90% gross margins for memory, but we'll get there." "And then at some point from there, it'll also halve back down to like 70s or maybe even lower. And so we'll see this oscillation in memory."

Fireside Alpha

17,338 просмотров • 10 дней назад

SK Hynix recently reached a $1 trillion market cap. SanDisk started the year at $35B and is now worth $230B. Dylan (Dylan Patel) on why memory prices will still double or triple from here: "People are like, the memory story is overplayed. No, you don't get it. DRAM will double or triple from here still, because that's how much capacity is required. Memory can only grow capacity low double digit percentages a year. 20-30% a year, even less for NAND, a little bit higher for DRAM. Even though the demand signal was very strong at the end of 2025, the memory companies immediately started reacting. None of that incremental capacity really gets here until 2028, even if they wanted to build as fast as possible. So the result is memory prices have gone through the roof. They're going to double and triple again. DRAM, especially. And they have to steal capacity from somewhere else. And the only way to steal capacity from somewhere else in a capitalist economy is demand destruction via higher pricing." Publish date: April 23, 2026
1:59

SK Hynix recently reached a $1 trillion market cap. SanDisk started the year at $35B and is now worth $230B. Dylan (Dylan Patel) on why memory prices will still double or triple from here: "People are like, the memory story is overplayed. No, you don't get it. DRAM will double or triple from here still, because that's how much capacity is required. Memory can only grow capacity low double digit percentages a year. 20-30% a year, even less for NAND, a little bit higher for DRAM. Even though the demand signal was very strong at the end of 2025, the memory companies immediately started reacting. None of that incremental capacity really gets here until 2028, even if they wanted to build as fast as possible. So the result is memory prices have gone through the roof. They're going to double and triple again. DRAM, especially. And they have to steal capacity from somewhere else. And the only way to steal capacity from somewhere else in a capitalist economy is demand destruction via higher pricing." Publish date: April 23, 2026

Invest Like the Best

105,024 просмотров • 1 месяц назад

Dylan on why memory prices will double again: "Memory can only grow capacity low double digit percentages a year. 20-30% a year, even less for NAND, a little bit higher for DRAM, but whatever. Even though the demand signal was very strong at the end of 2025, the memory companies immediately started reacting. None of that incremental capacity really gets here until 2028, even if they wanted to build as fast as possible. So the result is memory prices have gone through the roof. They're going to double and triple again. DRAM, especially. People are like, "oh, the memory story is overplayed." No, you don't get it. DRAM will double or triple from here still, because that's how much capacity is required. And they have to steal capacity from somewhere else. And the only way to steal capacity from somewhere else in a capitalist economy is demand destruction via higher pricing."
2:17

Dylan on why memory prices will double again: "Memory can only grow capacity low double digit percentages a year. 20-30% a year, even less for NAND, a little bit higher for DRAM, but whatever. Even though the demand signal was very strong at the end of 2025, the memory companies immediately started reacting. None of that incremental capacity really gets here until 2028, even if they wanted to build as fast as possible. So the result is memory prices have gone through the roof. They're going to double and triple again. DRAM, especially. People are like, "oh, the memory story is overplayed." No, you don't get it. DRAM will double or triple from here still, because that's how much capacity is required. And they have to steal capacity from somewhere else. And the only way to steal capacity from somewhere else in a capitalist economy is demand destruction via higher pricing."

Patrick OShaughnessy

189,478 просмотров • 3 месяцев назад

DeepMind CEO Demis Hassabis says the AI bottleneck is already moving from model ideas to physical supply chains. "How much of the restriction at this point is physical? I am talking memory shortage, GPU shortage, and electricity." Demis: "That is constraining a lot of the deployment for sure. We see so much more demand for our models - Gemini and the other models that we have - than we can possibly serve." Then the less obvious part: "It does constrain a little bit the research, because you need a lot of chips to be able to experiment on new ideas at big enough scale that you can actually see if they are going to work." And on memory: "The whole supply chain is kind of strained... It still in the end actually comes down to a few suppliers of a few of the key components. Anywhere where there is some kind of constraint on the capacity, there is a sort of choke point." Important framing: The next AI race is not just who has the best model architecture. It is who can secure memory, chips, power, suppliers, and enough experimental capacity to find the next breakthrough before everyone else.
1:09

DeepMind CEO Demis Hassabis says the AI bottleneck is already moving from model ideas to physical supply chains. "How much of the restriction at this point is physical? I am talking memory shortage, GPU shortage, and electricity." Demis: "That is constraining a lot of the deployment for sure. We see so much more demand for our models - Gemini and the other models that we have - than we can possibly serve." Then the less obvious part: "It does constrain a little bit the research, because you need a lot of chips to be able to experiment on new ideas at big enough scale that you can actually see if they are going to work." And on memory: "The whole supply chain is kind of strained... It still in the end actually comes down to a few suppliers of a few of the key components. Anywhere where there is some kind of constraint on the capacity, there is a sort of choke point." Important framing: The next AI race is not just who has the best model architecture. It is who can secure memory, chips, power, suppliers, and enough experimental capacity to find the next breakthrough before everyone else.

Karl Mehta

25,716 просмотров • 1 месяц назад

🚨 WHAT IF THE MEMORY SHORTAGE WAS MANUFACTURED? Samsung, SK Hynix, and Micron are being sued for allegedly engineering the DRAM shortage on purpose. The lawsuit claims they used the AI boom as cover to quietly cut normal memory supply. DRAM prices are up 500 to 700%. The damage is now showing up in iPhone and Mac prices. The makers say it's innocent: AI buyers showed up with huge money for HBM. But here's the bigger question. Memory stocks are priced for endless AI demand. If a court rules the scarcity was manufactured, how much of that demand was ever real?
0:37

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

CryptoGoos

32,615 просмотров • 1 месяц назад

Jensen Huang: “We need memory for… HBM memory for performance. We need LPDDR for low-power memory.” Reporter: Is memory constraint an issue for NVIDIA this year? Jensen Huang: “The entire supply chain is challenging this year because demand is so much more. Supply has been growing 100% every year, but demand is going faster than that. We are going to have a very good year.” We are entering a Memory Supercycle $MU
0:50

Jensen Huang: “We need memory for… HBM memory for performance. We need LPDDR for low-power memory.” Reporter: Is memory constraint an issue for NVIDIA this year? Jensen Huang: “The entire supply chain is challenging this year because demand is so much more. Supply has been growing 100% every year, but demand is going faster than that. We are going to have a very good year.” We are entering a Memory Supercycle $MU

NIK

217,217 просмотров • 6 месяцев назад

$MU CEO Sanjay Mehrotra said Micron can only supply ~60% of what key customers are requesting as AI demand creates an unprecedented memory shortage. Every wafer shifted into HBM can cost ~3x the standard DRAM output and demand keeps rising because inference, agents, robotics and autonomous systems are all memory-bandwidth-bound. Memory fab capacity takes three to five years to build so the shortage Mehrotra is describing today could define the supply curve for the rest of the decade.
0:28

$MU CEO Sanjay Mehrotra said Micron can only supply ~60% of what key customers are requesting as AI demand creates an unprecedented memory shortage. Every wafer shifted into HBM can cost ~3x the standard DRAM output and demand keeps rising because inference, agents, robotics and autonomous systems are all memory-bandwidth-bound. Memory fab capacity takes three to five years to build so the shortage Mehrotra is describing today could define the supply curve for the rest of the decade.

Shay Boloor

125,563 просмотров • 2 месяцев назад

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."
0:51

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."

Fireside Alpha

140,200 просмотров • 22 дней назад

"Everyone's making fun of these Koreans for being long memory stocks. But we're talking single-digit P/E multiples. It's probably going to last 2-3 more years. Triple digit growth." — tae kim "Layer that onto the fact that 4 years ago, all these memory companies saw their revenue get cut in half. So they didn't expand capacity. And it takes 3-4 years to expand capacity." "We're going to see mega pricing power. We haven't seen anything yet. These stocks are going to keep going higher, and their revenue rates are going to be astronomical."
1:32

"Everyone's making fun of these Koreans for being long memory stocks. But we're talking single-digit P/E multiples. It's probably going to last 2-3 more years. Triple digit growth." — tae kim "Layer that onto the fact that 4 years ago, all these memory companies saw their revenue get cut in half. So they didn't expand capacity. And it takes 3-4 years to expand capacity." "We're going to see mega pricing power. We haven't seen anything yet. These stocks are going to keep going higher, and their revenue rates are going to be astronomical."

TBPN

156,362 просмотров • 2 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

594,643 просмотров • 6 месяцев назад

AI is running out of short-term memory. We’re watching a global chip shortage play out in real time, and that tiny bottleneck is about to make everything more expensive. Who wins when the memory runs out?
1:19

AI is running out of short-term memory. We’re watching a global chip shortage play out in real time, and that tiny bottleneck is about to make everything more expensive. Who wins when the memory runs out?

MONIIFY

425,758 просмотров • 8 месяцев 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 • 1 месяц назад

The AI bottleneck is not power. It is memory. Intel CEO Lip Bu Tan Lip-Bu Tan: "Everybody knows right now memory is the biggest shortage." Building fab capacity to fix it takes years. HBM and DRAM tightness is the primary speed limit on AI cluster deployment right now. Power gets the headlines, memory gates the build. Corroborated: Marvell CEO Matt Murphy said the same at COMPUTEX a week earlier. Watch HBM and DRAM pricing as the binding gauge: Source: No Priors -
0:32

The AI bottleneck is not power. It is memory. Intel CEO Lip Bu Tan Lip-Bu Tan: "Everybody knows right now memory is the biggest shortage." Building fab capacity to fix it takes years. HBM and DRAM tightness is the primary speed limit on AI cluster deployment right now. Power gets the headlines, memory gates the build. Corroborated: Marvell CEO Matt Murphy said the same at COMPUTEX a week earlier. Watch HBM and DRAM pricing as the binding gauge: Source: No Priors -

Podcast Alpha

17,327 просмотров • 1 месяц назад

Semi Analysis Dylan Patel: People are like, 'oh, the memory story is overplayed, everyone gets it.' No, no, no — you don't get it. DRAM will double or triple from here still, because that's how much capacity is required, and they have to steal capacity from somewhere else. And the only way to steal capacity in a capitalist economy is demand destruction via higher pricing.
1:09

Semi Analysis Dylan Patel: People are like, 'oh, the memory story is overplayed, everyone gets it.' No, no, no — you don't get it. DRAM will double or triple from here still, because that's how much capacity is required, and they have to steal capacity from somewhere else. And the only way to steal capacity in a capitalist economy is demand destruction via higher pricing.

Wall St Engine

208,608 просмотров • 3 месяцев назад

"Modern people would say memory is not reliable — 'My memory is not good.' But the ancient people, it was so much different. Memory, it was a survival skill. It was crucial to ancient humans. Modern humans, I think we're becoming drones — we're outsourcing our memories to ChatGPT and AI and all this other stuff."
10:35

"Modern people would say memory is not reliable — 'My memory is not good.' But the ancient people, it was so much different. Memory, it was a survival skill. It was crucial to ancient humans. Modern humans, I think we're becoming drones — we're outsourcing our memories to ChatGPT and AI and all this other stuff."

Shawn Ryan

49,383 просмотров • 2 месяцев назад

Bubble Boi reveals the Kimi breakthrough that made him sell his entire portfolio overnight "Kimi is using a technique called linear attention that cuts down memory growth entirely to a fixed size, and they didn't just get okay results, they got world-class results. This same technique was used by the Qwen models but nobody thought it would scale. It's looking more and more likely that it scales pretty well, and if that's the case the memory per user is always fixed." "So if every frontier model goes to this, the demand for DRAM drops significantly. They already bought the memory, they just serve more and more users. It's like you had a car and someone found a way to make it 10 times faster, you're not going to buy a new car." "But you also have to readjust the assumption that hyperscalers are going to crazily spend for the next two years. What's to say there's not another innovation that shrinks this more and now you go from serving a thousand users to a hundred thousand users on one server rack? How do you justify spending 500k on a new rack at that point?"
3:10

Bubble Boi reveals the Kimi breakthrough that made him sell his entire portfolio overnight "Kimi is using a technique called linear attention that cuts down memory growth entirely to a fixed size, and they didn't just get okay results, they got world-class results. This same technique was used by the Qwen models but nobody thought it would scale. It's looking more and more likely that it scales pretty well, and if that's the case the memory per user is always fixed." "So if every frontier model goes to this, the demand for DRAM drops significantly. They already bought the memory, they just serve more and more users. It's like you had a car and someone found a way to make it 10 times faster, you're not going to buy a new car." "But you also have to readjust the assumption that hyperscalers are going to crazily spend for the next two years. What's to say there's not another innovation that shrinks this more and now you go from serving a thousand users to a hundred thousand users on one server rack? How do you justify spending 500k on a new rack at that point?"

Kai

388,360 просмотров • 22 дней назад

What is your earliest memory? Memory in young children is a fascinating - and complex - topic. On one hand, newborns exhibit immediate recognition of their mothers’ voices and music played regularly while they were in utero. And within days they can recall their mother’s faces and distinguish them from those of others. Clearly newborns have some capacity for memory. But this early sensory memory is different from the long term, autobiographical memory we depend on to recall our lives’ most significant events. Indeed, few adults can claim memories earlier than 2-3 years of age. Why don’t we remember our earliest years? Researchers point to a couple of potential factors, beginning with language. As adults we rely heavily on language to organize our autobiographical memories into narratives, but this capacity is still developing during infancy. In addition, there is the issue of the hippocampus, the region of the brain responsible for converting short-term memories into long-term memories - which, during infancy, is still very much in development itself. The result is a phenomenon sometimes called infantile amnesia. So take plenty of videos to share with your child as they get older, because the most vivid memories of their early years are likely to be yours. How about you? What is your earliest memory and how old would you have been at the time? This adorable father/son conversation was shared to IG by Owens.Shaun.
0:29

What is your earliest memory? Memory in young children is a fascinating - and complex - topic. On one hand, newborns exhibit immediate recognition of their mothers’ voices and music played regularly while they were in utero. And within days they can recall their mother’s faces and distinguish them from those of others. Clearly newborns have some capacity for memory. But this early sensory memory is different from the long term, autobiographical memory we depend on to recall our lives’ most significant events. Indeed, few adults can claim memories earlier than 2-3 years of age. Why don’t we remember our earliest years? Researchers point to a couple of potential factors, beginning with language. As adults we rely heavily on language to organize our autobiographical memories into narratives, but this capacity is still developing during infancy. In addition, there is the issue of the hippocampus, the region of the brain responsible for converting short-term memories into long-term memories - which, during infancy, is still very much in development itself. The result is a phenomenon sometimes called infantile amnesia. So take plenty of videos to share with your child as they get older, because the most vivid memories of their early years are likely to be yours. How about you? What is your earliest memory and how old would you have been at the time? This adorable father/son conversation was shared to IG by Owens.Shaun.

Dan Wuori

169,714 просмотров • 2 лет 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

93,627 просмотров • 1 месяц 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 • 2 месяцев назад

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:
0:40

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

28,362 просмотров • 11 дней назад