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Micron's Sanjay Mehrotra on memory: "Memory is no longer a component in a system, memory is the strategic infrastructure for AI. It is no longer a commodity, it is a high value. Without memory, you cannot make AI smarter, cannot make it faster, you cannot scale up AI...AI is... show more

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$MU CEO Sanjay Mehrotra says AI is turning memory from a commodity into “strategic infrastructure.” AI cannot scale without more HBM DRAM and SSD capacity which is making memory far more valuable across the entire compute stack.

$MU CEO Sanjay Mehrotra says AI is turning memory from a commodity into “strategic infrastructure.” AI cannot scale without more HBM DRAM and SSD capacity which is making memory far more valuable across the entire compute stack.

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$INTC CEO on memory, live on the earnings call: "the memory [has] become the big supply constraint challenge, and we [are] collaborating with the three big memory vendors. ... Clearly memory, it [has] become the bottleneck [for] a lot of AI infrastructure and pinpoint for customer." $MU $SKHY $DRAM $SNDK

$INTC CEO on memory, live on the earnings call: "the memory [has] become the big supply constraint challenge, and we [are] collaborating with the three big memory vendors. ... Clearly memory, it [has] become the bottleneck [for] a lot of AI infrastructure and pinpoint for customer." $MU $SKHY $DRAM $SNDK

Trade Whisperer

122,629 просмотров • 29 дней 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 -

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 • 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:

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

78,811 просмотров • 17 дней 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."

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 • 28 дней назад

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

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

TheValueist

23,901 просмотров • 7 месяцев назад

Introducing Memory 101 🧠 Every ai tool you use is a goldfish. brilliant for one chat, close the tab and it forgets you completely. memory is the most underbuilt layer in ai right now. so we're explaining how it actually works, one episode at a time. coming up: → rag vs memory, why most "ai memory" is just search → the 5 things real memory needs: facts, profiles, graphs, time, forgetting → why good memory has to forget things follow along!

Introducing Memory 101 🧠 Every ai tool you use is a goldfish. brilliant for one chat, close the tab and it forgets you completely. memory is the most underbuilt layer in ai right now. so we're explaining how it actually works, one episode at a time. coming up: → rag vs memory, why most "ai memory" is just search → the 5 things real memory needs: facts, profiles, graphs, time, forgetting → why good memory has to forget things follow along!

supermemory

17,235 просмотров • 1 месяц назад

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

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

Shay Boloor

306,107 просмотров • 2 месяцев назад

Creator of High Bandwidth Memory: "How much HBM a country possesses determines national AI hegemony and the competitiveness of AI companies. Memory determines which is better: Google Gemini, OpenAI, or Anthropic Claude."

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Fireside Alpha

98,087 просмотров • 1 месяц назад

What a disaster... People are bout to realize "AI" is just a commodity like memory, and there is no moat Andrei Jikh

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Financelot

218,065 просмотров • 1 месяц 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.

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

BREAKING: The next big constraint and bottleneck coming for the AI industry is MEMORY (DRAM) says Macquarie Group Only 3 companies make it, and they are short. What is DRAM? Who controls the supply? What does this mean for demand for power and AI infrastructure?👇 $HUT Hut 8

BREAKING: The next big constraint and bottleneck coming for the AI industry is MEMORY (DRAM) says Macquarie Group Only 3 companies make it, and they are short. What is DRAM? Who controls the supply? What does this mean for demand for power and AI infrastructure?👇 $HUT Hut 8

Sue Ennis

117,445 просмотров • 7 месяцев назад

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

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

Damir Akaza

821,815 просмотров • 1 месяц назад

Memory is eating the world When consumers feel the heat, you know its a problem. While big-3 memory companies are enjoying sweet-sweet profit margins…the rest of the electronics market is cracking under pricing pressure. - GoPro is more like GoingUnder - Apple products 10-30% pricier - Even music synths are more expensive Is it price gouging at this point? Is this revenge for all the memory cyclicality? When will this end? Chapters: 0:00 Memory crisis hits 1:20 AI impacting consumers 3:00 AI causing inflation 6:48 Consumer demand drop? 8:49 AI demand inelastic 10:56 Long-term memory outlook 11:02 GoPro's memory woes 12:24 Apple's pricing power 18:20 Apple seeks CXMT DRAM 21:38 Shrinkflation for phones 23:16 Korea's memory investment 26:10 Micron's killing profits 33:00 Why AI needs so much DRAM 40:27 Future of AI training 44:34 Cost-optimizing inference Get more of Austin and Vik daily, free! Sign up: Connect with Vik and Austin: Vik's Paid Substack: Austin's Paid Substack: Austin Lyons Vikram Sekar $MU

Memory is eating the world When consumers feel the heat, you know its a problem. While big-3 memory companies are enjoying sweet-sweet profit margins…the rest of the electronics market is cracking under pricing pressure. - GoPro is more like GoingUnder - Apple products 10-30% pricier - Even music synths are more expensive Is it price gouging at this point? Is this revenge for all the memory cyclicality? When will this end? Chapters: 0:00 Memory crisis hits 1:20 AI impacting consumers 3:00 AI causing inflation 6:48 Consumer demand drop? 8:49 AI demand inelastic 10:56 Long-term memory outlook 11:02 GoPro's memory woes 12:24 Apple's pricing power 18:20 Apple seeks CXMT DRAM 21:38 Shrinkflation for phones 23:16 Korea's memory investment 26:10 Micron's killing profits 33:00 Why AI needs so much DRAM 40:27 Future of AI training 44:34 Cost-optimizing inference Get more of Austin and Vik daily, free! Sign up: Connect with Vik and Austin: Vik's Paid Substack: Austin's Paid Substack: Austin Lyons Vikram Sekar $MU

Semi Doped

34,810 просмотров • 1 месяц назад

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

Fireside Alpha

38,370 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?

🚨 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

$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.

$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 • 3 месяцев назад

🚨 BULLISH ON $MU 🚨 $MU is the only U.S.-based memory chip manufacturer with leading-edge DRAM and NAND production at scale. $SPCX Elon Musk highlighted the strategic importance of domestic semiconductor manufacturing as AI demand continues to explode. 👇. He mentioned and confirmed $MU is only Memory maker in US till 2029-2030, no other company exist till next few years 🔥🤯

🚨 BULLISH ON $MU 🚨 $MU is the only U.S.-based memory chip manufacturer with leading-edge DRAM and NAND production at scale. $SPCX Elon Musk highlighted the strategic importance of domestic semiconductor manufacturing as AI demand continues to explode. 👇. He mentioned and confirmed $MU is only Memory maker in US till 2029-2030, no other company exist till next few years 🔥🤯

investing

837,965 просмотров • 2 месяцев назад

OpenClaw's memory out of the box sucks With this simple Obsidian system, it's actually awesome In this video I go over using Obsidian as a memory layer for your AI agent, making it SO much more smarter than it is now If you use OpenClaw or Hermes, you need to watch this:

OpenClaw's memory out of the box sucks With this simple Obsidian system, it's actually awesome In this video I go over using Obsidian as a memory layer for your AI agent, making it SO much more smarter than it is now If you use OpenClaw or Hermes, you need to watch this:

Alex Finn

89,742 просмотров • 4 месяцев назад