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$MRVL sits on both sides of the AI bottleneck at the same time (Save this). "Computing at this scale is fundamentally a connectivity challenge." That is Marvell CEO Matt Murphy explaining why the bottleneck has shifted from compute to connectivity. Marvell is a fabless chip designer meaning it designs...

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165,662 次观看 • 2 个月前 •via X (Twitter)

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$MRVL is the biggest position in Jordi Visser's portfolio. He believes that Marvell is going to significantly benefit from Nvidia's Vera Rubin platform. Right now, Marvell has 18 active custom chip programs and a dozen of them sit inside the four biggest hyperscalers on the planet. Management just guided fiscal 2027 revenue to $11.5 billion, up 40% year over year. Revenue moves from $8.7 billion over the last twelve months to $16.7 billion by fiscal 2028 and $23.3 billion by fiscal 2029, nearly tripling in three years. Jensen Huang thinks $MRVL will become a trillion dollar company in the next 3 years. Valued at $225B today, that's 4x trade for everyone who gets in now. Jensen has also invested $2 billion into Marvell in March. When a hyperscaler hires Marvell to build its AI accelerator, Marvell also sells the switches, the optical interconnects, the memory fabric and the retimers that go around it. One customer win turns into four or five revenue streams. Our analysts remain bullish on $MRVL. Jordi Visser's two biggest positions are $MRVL and $LLY. Our analysts hold both assets in their portfolios. Start your $1 trial and see the full portfolio of each of our five analysts. Link in first comment below.
1:02

$MRVL is the biggest position in Jordi Visser's portfolio. He believes that Marvell is going to significantly benefit from Nvidia's Vera Rubin platform. Right now, Marvell has 18 active custom chip programs and a dozen of them sit inside the four biggest hyperscalers on the planet. Management just guided fiscal 2027 revenue to $11.5 billion, up 40% year over year. Revenue moves from $8.7 billion over the last twelve months to $16.7 billion by fiscal 2028 and $23.3 billion by fiscal 2029, nearly tripling in three years. Jensen Huang thinks $MRVL will become a trillion dollar company in the next 3 years. Valued at $225B today, that's 4x trade for everyone who gets in now. Jensen has also invested $2 billion into Marvell in March. When a hyperscaler hires Marvell to build its AI accelerator, Marvell also sells the switches, the optical interconnects, the memory fabric and the retimers that go around it. One customer win turns into four or five revenue streams. Our analysts remain bullish on $MRVL. Jordi Visser's two biggest positions are $MRVL and $LLY. Our analysts hold both assets in their portfolios. Start your $1 trial and see the full portfolio of each of our five analysts. Link in first comment below.

Milk Road Stocks

53,847 次观看 • 1 个月前

Jensen Huang is investing in every photonics company he can find and the reason why tells you everything about where AI is headed (Save this). Lip-Bu Tan, the CEO of Intel says, when he looks for investment opportunities, he looks for the bottleneck and right now, the bottleneck is the interconnect, the pipes that move data between chips inside an AI data center. That is why he backed Credo Semiconductor, Astera Labs and Celestial AI on the optical side. Here is the simple version of what the interconnect bottleneck actually means. Think of an AI data center like a city, the GPUs are the buildings where all the work happens but for those buildings to function, you need roads connecting them, fast roads that can carry enormous traffic without congestion. And those roads are now the single biggest constraint on AI performance. As clusters scale to hundreds of thousands of GPUs, traditional copper wiring is hitting its physical limits and that is where this entire sector comes in. Credo Semiconductor (CRDO) is the most direct pure play on this theme, Credo makes high speed cables and optical chips that connect GPUs inside data center racks. Their revenue tripled in fiscal 2026 to $1.3 billion, growing 272% year over year at its peak and four of the world's largest hyperscalers each individually account for more than 10% of Credo's revenue. Astera Labs (ALAB) solves the connection problem between different chip types. Astera makes the PCIe and connectivity chips that manage data flow between GPUs, CPUs, and memory without errors or slowdowns. Their revenue grew 93% year over year to $308 million in Q1 2026 alone. The optical companies are where the longer-term and potentially larger opportunity lives. Copper has physical limits, you can only push electrical signals so far before the signal degrades, the heat spikes and power consumption explodes. The solution is light, fiber optic connections that move data using photons instead of electrons which is faster, cooler and far more energy efficient. Jensen Huang made this clear at Computex 2026 because copper works as long as physically possible but at greater distances and larger scale, optics takes over. Coherent (COHR) is the most established optical company in this space. Coherent makes the lasers, transceivers, and optical components at the foundation of all fiber optic communications. Nvidia signed a multibillion-dollar purchase commitment and invested $2 billion directly into the company and their customer order books are already extending out to 2028. Marvell (MRVL) is the most comprehensive bet across the entire connectivity stack. Marvell makes chips for optical networking, PCIe switching and custom AI silicon. Jensen Huang called Marvell the next trillion dollar company at Computex 2026 and backed it with a $2 billion Nvidia investment. Marvell also acquired Celestial AI, the exact company Lip-Bu Tan backed for $3.25 billion, gaining photonic fabric technology delivering 16 terabits per second of bandwidth. Lumentum (LITE), Corning (GLW), and Ciena (CIEN) round out the major public names. Lumentum received a $2 billion Nvidia investment for laser and photonics components. Corning known mostly for phone glass received $500 million from Nvidia for optical connectivity work and is up over 100% year to date. Ciena runs the optical networking systems between data centers and is seeing analyst price targets raised on the back of the AI optics boom. Every time a hyperscaler spends a billion dollars on Nvidia GPUs, the surrounding infrastructure, cables, switches, transceivers, optical components has to be upgraded to match. The smarter the GPU gets, the more the interconnect matters. Nvidia has committed at least $6.5 billion to photonics companies in the past 4 months alone and the companies building the roads between the GPUs may end up being just as valuable as the companies building the GPUs themselves. Follow me Melvin for more AI, semis and the next big market themes.
0:29

Jensen Huang is investing in every photonics company he can find and the reason why tells you everything about where AI is headed (Save this). Lip-Bu Tan, the CEO of Intel says, when he looks for investment opportunities, he looks for the bottleneck and right now, the bottleneck is the interconnect, the pipes that move data between chips inside an AI data center. That is why he backed Credo Semiconductor, Astera Labs and Celestial AI on the optical side. Here is the simple version of what the interconnect bottleneck actually means. Think of an AI data center like a city, the GPUs are the buildings where all the work happens but for those buildings to function, you need roads connecting them, fast roads that can carry enormous traffic without congestion. And those roads are now the single biggest constraint on AI performance. As clusters scale to hundreds of thousands of GPUs, traditional copper wiring is hitting its physical limits and that is where this entire sector comes in. Credo Semiconductor (CRDO) is the most direct pure play on this theme, Credo makes high speed cables and optical chips that connect GPUs inside data center racks. Their revenue tripled in fiscal 2026 to $1.3 billion, growing 272% year over year at its peak and four of the world's largest hyperscalers each individually account for more than 10% of Credo's revenue. Astera Labs (ALAB) solves the connection problem between different chip types. Astera makes the PCIe and connectivity chips that manage data flow between GPUs, CPUs, and memory without errors or slowdowns. Their revenue grew 93% year over year to $308 million in Q1 2026 alone. The optical companies are where the longer-term and potentially larger opportunity lives. Copper has physical limits, you can only push electrical signals so far before the signal degrades, the heat spikes and power consumption explodes. The solution is light, fiber optic connections that move data using photons instead of electrons which is faster, cooler and far more energy efficient. Jensen Huang made this clear at Computex 2026 because copper works as long as physically possible but at greater distances and larger scale, optics takes over. Coherent (COHR) is the most established optical company in this space. Coherent makes the lasers, transceivers, and optical components at the foundation of all fiber optic communications. Nvidia signed a multibillion-dollar purchase commitment and invested $2 billion directly into the company and their customer order books are already extending out to 2028. Marvell (MRVL) is the most comprehensive bet across the entire connectivity stack. Marvell makes chips for optical networking, PCIe switching and custom AI silicon. Jensen Huang called Marvell the next trillion dollar company at Computex 2026 and backed it with a $2 billion Nvidia investment. Marvell also acquired Celestial AI, the exact company Lip-Bu Tan backed for $3.25 billion, gaining photonic fabric technology delivering 16 terabits per second of bandwidth. Lumentum (LITE), Corning (GLW), and Ciena (CIEN) round out the major public names. Lumentum received a $2 billion Nvidia investment for laser and photonics components. Corning known mostly for phone glass received $500 million from Nvidia for optical connectivity work and is up over 100% year to date. Ciena runs the optical networking systems between data centers and is seeing analyst price targets raised on the back of the AI optics boom. Every time a hyperscaler spends a billion dollars on Nvidia GPUs, the surrounding infrastructure, cables, switches, transceivers, optical components has to be upgraded to match. The smarter the GPU gets, the more the interconnect matters. Nvidia has committed at least $6.5 billion to photonics companies in the past 4 months alone and the companies building the roads between the GPUs may end up being just as valuable as the companies building the GPUs themselves. Follow me Melvin for more AI, semis and the next big market themes.

Melvin

152,995 次观看 • 2 个月前

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.
1:01

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.

Ricardo

162,447 次观看 • 4 个月前

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.
0:41

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.

The Assembly

7,307,633 次观看 • 4 个月前

Nvidia's next generation chips are about to make a handful of companies impossible to ignore and here is how you can benefit from this (Save this). Goldman projects networking content per AI compute system could rise from $315,000 today to $9.4 million in Nvidia's next generation, a 29x increase in what companies spend just wiring their chips together. Morgan Stanley, Citi and Goldman all agree on the same direction, projecting the total addressable market for AI networking to grow from around $11 to $15 billion today to $154 billion by 2028, roughly a 9x jump in just a few years. As AI clusters scale into the hundreds of thousands of GPUs, the chips themselves stop being the bottleneck and the connections between them become the limiting factor instead. Every GPU needs to talk to every other GPU almost instantly to keep a training run synchronized and that requires far more sophisticated switches, optical modules and cabling than today's networks can provide, which is exactly why Nvidia's next generation systems are expected to need dramatically more networking hardware per system just to keep up. Optical modules specifically are becoming the chokepoint. The global optical module total addressable market is projected to grow from roughly $6.7 billion in the first quarter of 2025 to nearly $69 billion by 2028, with 1.6T modules going from essentially zero market share today to capturing the majority of shipments by 2028 as speeds keep climbing. Nvidia sits at the center of this story since it designs the switches and networking silicon that go into every one of these systems, meaning that 29x jump in networking content per system flows straight into Nvidia's own revenue per AI factory, not just its GPU sales. Marvell is one of the biggest direct beneficiaries here, since it makes the custom networking chips and interconnect silicon that hyperscalers rely on to move data between GPUs at these speeds. Credo Technology plays a slightly different role, supplying the high speed connectivity chips that clean up signals moving through copper links, which becomes more valuable as networks push toward higher bandwidth with lower power draw. AAOI, or Applied Optoelectronics, sits closer to the physical optics layer, making the lasers and optical components that go into the transceivers carrying data across these networks, positioning it directly in the path of that jump from $6.7 billion to $69 billion in optical module demand. Coherent and Lumentum round out the optical side, both supplying lasers and photonic components that scale directly with 1.6T module adoption as it goes from near zero to the majority of shipments by 2028. Switch and connector makers benefit almost mechanically from this trend too, since every additional dollar of networking content per system has to physically pass through a switch, a cable, or a connector, meaning revenue scales with network complexity regardless of which chipmaker's GPUs sit inside the rack. Bullish on AI networking, make sure to follow Melvin for more semiconductor insights and check out the link below for more details.
3:38

Nvidia's next generation chips are about to make a handful of companies impossible to ignore and here is how you can benefit from this (Save this). Goldman projects networking content per AI compute system could rise from $315,000 today to $9.4 million in Nvidia's next generation, a 29x increase in what companies spend just wiring their chips together. Morgan Stanley, Citi and Goldman all agree on the same direction, projecting the total addressable market for AI networking to grow from around $11 to $15 billion today to $154 billion by 2028, roughly a 9x jump in just a few years. As AI clusters scale into the hundreds of thousands of GPUs, the chips themselves stop being the bottleneck and the connections between them become the limiting factor instead. Every GPU needs to talk to every other GPU almost instantly to keep a training run synchronized and that requires far more sophisticated switches, optical modules and cabling than today's networks can provide, which is exactly why Nvidia's next generation systems are expected to need dramatically more networking hardware per system just to keep up. Optical modules specifically are becoming the chokepoint. The global optical module total addressable market is projected to grow from roughly $6.7 billion in the first quarter of 2025 to nearly $69 billion by 2028, with 1.6T modules going from essentially zero market share today to capturing the majority of shipments by 2028 as speeds keep climbing. Nvidia sits at the center of this story since it designs the switches and networking silicon that go into every one of these systems, meaning that 29x jump in networking content per system flows straight into Nvidia's own revenue per AI factory, not just its GPU sales. Marvell is one of the biggest direct beneficiaries here, since it makes the custom networking chips and interconnect silicon that hyperscalers rely on to move data between GPUs at these speeds. Credo Technology plays a slightly different role, supplying the high speed connectivity chips that clean up signals moving through copper links, which becomes more valuable as networks push toward higher bandwidth with lower power draw. AAOI, or Applied Optoelectronics, sits closer to the physical optics layer, making the lasers and optical components that go into the transceivers carrying data across these networks, positioning it directly in the path of that jump from $6.7 billion to $69 billion in optical module demand. Coherent and Lumentum round out the optical side, both supplying lasers and photonic components that scale directly with 1.6T module adoption as it goes from near zero to the majority of shipments by 2028. Switch and connector makers benefit almost mechanically from this trend too, since every additional dollar of networking content per system has to physically pass through a switch, a cable, or a connector, meaning revenue scales with network complexity regardless of which chipmaker's GPUs sit inside the rack. Bullish on AI networking, make sure to follow Melvin for more semiconductor insights and check out the link below for more details.

Melvin

42,220 次观看 • 2 个月前

Google just admitted it can't build data centers fast enough, so it's planning on baking its AI model directly into the chip instead (Save this). Google's new Frozen v2 chip permanently embeds parts of Gemini's architecture into the silicon itself, cutting down on the calculations and data movement needed to answer a query. Engineers estimate it could process 6 to 10 times more tokens per unit of power than Google's current TPUs. The real story is why Google is building this in the first place because Frozen v2 is meant to ease a severe internal compute crunch that's caused friction between teams at Google. It reportedly pushed Google Cloud to turn away outside business because it simply doesn't have enough spare capacity to go around. If Google, one of the largest chipmakers in the world, is short enough on compute to turn away paying Cloud customers, that's confirmation this shortage isn't a scaling problem unique to smaller players, it's systemic across the entire industry. This ties into a much bigger power struggle happening right now. More AI companies are trying to cut their reliance on Nvidia by building their own chips. OpenAI rolled out a custom chip called Jalapeno alongside Broadcom last month and Anthropic is now partnering with Samsung on something similar. The reasoning is pretty simple, Nvidia effectively acts as landlord for every hyperscaler out there, and the rent isn't cheap. Nvidia hardware can account for anywhere from 20% to 60% of total AI infrastructure spend, and once a company is tied into its ecosystem, every hardware refresh forces another costly one. That's exactly why Google built TPUs and Amazon built Trainium, both trying to protect their own margins for shareholders. Bullish on Marvell + Broadcom who makes these custom chips and follow me Melvin for more infrastructure plays and check out the link below for more!
2:52

Google just admitted it can't build data centers fast enough, so it's planning on baking its AI model directly into the chip instead (Save this). Google's new Frozen v2 chip permanently embeds parts of Gemini's architecture into the silicon itself, cutting down on the calculations and data movement needed to answer a query. Engineers estimate it could process 6 to 10 times more tokens per unit of power than Google's current TPUs. The real story is why Google is building this in the first place because Frozen v2 is meant to ease a severe internal compute crunch that's caused friction between teams at Google. It reportedly pushed Google Cloud to turn away outside business because it simply doesn't have enough spare capacity to go around. If Google, one of the largest chipmakers in the world, is short enough on compute to turn away paying Cloud customers, that's confirmation this shortage isn't a scaling problem unique to smaller players, it's systemic across the entire industry. This ties into a much bigger power struggle happening right now. More AI companies are trying to cut their reliance on Nvidia by building their own chips. OpenAI rolled out a custom chip called Jalapeno alongside Broadcom last month and Anthropic is now partnering with Samsung on something similar. The reasoning is pretty simple, Nvidia effectively acts as landlord for every hyperscaler out there, and the rent isn't cheap. Nvidia hardware can account for anywhere from 20% to 60% of total AI infrastructure spend, and once a company is tied into its ecosystem, every hardware refresh forces another costly one. That's exactly why Google built TPUs and Amazon built Trainium, both trying to protect their own margins for shareholders. Bullish on Marvell + Broadcom who makes these custom chips and follow me Melvin for more infrastructure plays and check out the link below for more!

Melvin

63,457 次观看 • 2 个月前

In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!
3:05

In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!

Milk Road AI

21,550 次观看 • 3 个月前

$MRVL went from $90 to $300 in 3 months. Jordi Visser Jordi Visser says the real move hasn't started. The Vera Rubin architecture ramp - the optical networking wave that drives Marvell's core revenue - hasn't begun yet. Jensen Huang called Marvell a trillion-dollar company at Computex. NVIDIA put $2 billion in. Marvell is currently at $275 billion. There's also no blowout earnings quarter yet. Jordi has watched that print precede sustained moves in every other AI infrastructure name - Dell, others. Marvell hasn't hit it. You need two of three catalysts (Vera Rubin ramp, blowout earnings, Jensen's public call) to be wrong for the thesis to break. Phil Rosen Phil Rosen has the full position map on Full Signal: Source: Full Signal -
2:34

$MRVL went from $90 to $300 in 3 months. Jordi Visser Jordi Visser says the real move hasn't started. The Vera Rubin architecture ramp - the optical networking wave that drives Marvell's core revenue - hasn't begun yet. Jensen Huang called Marvell a trillion-dollar company at Computex. NVIDIA put $2 billion in. Marvell is currently at $275 billion. There's also no blowout earnings quarter yet. Jordi has watched that print precede sustained moves in every other AI infrastructure name - Dell, others. Marvell hasn't hit it. You need two of three catalysts (Vera Rubin ramp, blowout earnings, Jensen's public call) to be wrong for the thesis to break. Phil Rosen Phil Rosen has the full position map on Full Signal: Source: Full Signal -

Podcast Alpha

56,796 次观看 • 3 个月前

Broadcom's CEO just exposed the real fight underneath Google's AI chip strategy. It is not Google versus Broadcom. It is Google and Broadcom trying to make Nvidia replaceable. Within two minutes at Bloomberg Tech, Hock Tan was asked whether Google bringing more chip design in house keeps him up at night. His exact words: "So we just compete against my own customer." Then he named the real enemy: "the real competitor facing all this is the GPU out of Nvidia." That is the part most people miss. Custom AI chips are not just cheaper GPUs. They are ownership claims. If Google owns the workload, the compiler stack, the cloud customer, and the TPU roadmap, Nvidia becomes a benchmark instead of the toll booth. But Broadcom is still in the room because independence is not binary. The hard part is not drawing a chip. The hard part is shipping generation after generation at scale, matching Nvidia's cadence, keeping networking tight, and making the whole system useful enough that developers do not care what silicon sits underneath. That is why Tan can say Google is trying to create customer owned tooling and still sound calm. Broadcom is not selling picks and shovels. It is selling the bridge out of Nvidia dependency. The numbers explain why this is suddenly a board level issue. Broadcom reported $22.2 billion of Q2 2026 revenue. Its AI semiconductor revenue hit $10.8 billion, up 143 percent year over year. For Q3, Broadcom guided AI semiconductor revenue to $16.0 billion, up more than 200 percent year over year. In the clip, Tan says Broadcom has exactly 6 custom AI accelerator customers. He says OpenAI has been engaged for over 2 years, its accelerator is already working in labs and data centers, and production is on track for late this year. That is the hidden mechanism: The AI labs are not becoming software companies with some chips attached. They are becoming capacity companies with model interfaces attached. Once your margin depends on tokens, latency, memory bandwidth, power contracts, packaging slots, networking gear, and a private accelerator schedule, the "model company" label starts to look like a costume. The precedent is Apple. Apple did not move into custom silicon because it wanted a cute chip branding story. It moved because the iPhone needed control over performance per watt, release cadence, and differentiation. A series chips in 2010. M1 in 2020. More than a decade of slowly pulling the bottleneck inside the company. But Apple still needed TSMC. That is the useful analogy for Google, OpenAI, and the other AI giants. They want Nvidia's margin pool. They want Nvidia's roadmap power. They want Nvidia's ability to decide who gets capacity first. But the first supplier they replace becomes the supplier they cannot live without. Broadcom is the customs officer at the border of private silicon. Second order consequence: AI company valuation will shift from model demos to infrastructure custody. Who owns the workload? Who controls the accelerator roadmap? Who has memory secured? Who can afford to keep a bad first generation alive long enough to get to the second and third? My bet: by the end of 2027, at least one major AI lab will be judged more by its custom chip execution than by its model benchmark lead. The model race is public. The margin race is being negotiated in silicon.
0:55

Broadcom's CEO just exposed the real fight underneath Google's AI chip strategy. It is not Google versus Broadcom. It is Google and Broadcom trying to make Nvidia replaceable. Within two minutes at Bloomberg Tech, Hock Tan was asked whether Google bringing more chip design in house keeps him up at night. His exact words: "So we just compete against my own customer." Then he named the real enemy: "the real competitor facing all this is the GPU out of Nvidia." That is the part most people miss. Custom AI chips are not just cheaper GPUs. They are ownership claims. If Google owns the workload, the compiler stack, the cloud customer, and the TPU roadmap, Nvidia becomes a benchmark instead of the toll booth. But Broadcom is still in the room because independence is not binary. The hard part is not drawing a chip. The hard part is shipping generation after generation at scale, matching Nvidia's cadence, keeping networking tight, and making the whole system useful enough that developers do not care what silicon sits underneath. That is why Tan can say Google is trying to create customer owned tooling and still sound calm. Broadcom is not selling picks and shovels. It is selling the bridge out of Nvidia dependency. The numbers explain why this is suddenly a board level issue. Broadcom reported $22.2 billion of Q2 2026 revenue. Its AI semiconductor revenue hit $10.8 billion, up 143 percent year over year. For Q3, Broadcom guided AI semiconductor revenue to $16.0 billion, up more than 200 percent year over year. In the clip, Tan says Broadcom has exactly 6 custom AI accelerator customers. He says OpenAI has been engaged for over 2 years, its accelerator is already working in labs and data centers, and production is on track for late this year. That is the hidden mechanism: The AI labs are not becoming software companies with some chips attached. They are becoming capacity companies with model interfaces attached. Once your margin depends on tokens, latency, memory bandwidth, power contracts, packaging slots, networking gear, and a private accelerator schedule, the "model company" label starts to look like a costume. The precedent is Apple. Apple did not move into custom silicon because it wanted a cute chip branding story. It moved because the iPhone needed control over performance per watt, release cadence, and differentiation. A series chips in 2010. M1 in 2020. More than a decade of slowly pulling the bottleneck inside the company. But Apple still needed TSMC. That is the useful analogy for Google, OpenAI, and the other AI giants. They want Nvidia's margin pool. They want Nvidia's roadmap power. They want Nvidia's ability to decide who gets capacity first. But the first supplier they replace becomes the supplier they cannot live without. Broadcom is the customs officer at the border of private silicon. Second order consequence: AI company valuation will shift from model demos to infrastructure custody. Who owns the workload? Who controls the accelerator roadmap? Who has memory secured? Who can afford to keep a bad first generation alive long enough to get to the second and third? My bet: by the end of 2027, at least one major AI lab will be judged more by its custom chip execution than by its model benchmark lead. The model race is public. The margin race is being negotiated in silicon.

Andrej Drats

10,572 次观看 • 2 个月前

Marvell spent $36 billion over 10 years to own every connectivity distance in a data center. $22.5B in acquisitions. $18B in organic R&D. Cavium for compute and networking. Inphi for $10B for data center interconnect. Celestial AI for photonic fabric. Xcon for scale-up switching. And one technical bet that could have destroyed the company: skipping the 7nm process node entirely, jumping from 14nm straight to 5nm. Murphy on stage: "Nobody does this. Nobody takes that kind of risk. But we did and it worked - flawlessly." That node-skip is what got Marvell into the hyperscaler qualification cycles driving 75%+ of revenue today. The full picture on what $36B built and whether it is defensible: Source: COMPUTEX 2026 Keynote -
1:36

Marvell spent $36 billion over 10 years to own every connectivity distance in a data center. $22.5B in acquisitions. $18B in organic R&D. Cavium for compute and networking. Inphi for $10B for data center interconnect. Celestial AI for photonic fabric. Xcon for scale-up switching. And one technical bet that could have destroyed the company: skipping the 7nm process node entirely, jumping from 14nm straight to 5nm. Murphy on stage: "Nobody does this. Nobody takes that kind of risk. But we did and it worked - flawlessly." That node-skip is what got Marvell into the hyperscaler qualification cycles driving 75%+ of revenue today. The full picture on what $36B built and whether it is defensible: Source: COMPUTEX 2026 Keynote -

Podcast Alpha

17,441 次观看 • 3 个月前

SITUATION EXPLAINED: The Trump administration is closing the loophole that lets Chinese labs rent US chips. • A small group inside Commerce is building a slimmed-down replacement for the Biden-era AI Diffusion Rule, which the administration scrapped in 2025 • The target is Chinese AI firms accessing advanced chips remotely through data centers abroad, in countries including Thailand and Singapore • The current regime is upside down: the US now permits H200 sales to China while Beijing forbids its own firms from buying them • Nvidia's share of China's AI chip market fell from 95% in 2023 to 8% in 2026, while Huawei climbed to roughly 50-60% Theo Jaffee: "It's kind of unclear to me how important access to American chips is for Chinese AI progress, especially if GLM 5.3 is served entirely on Chinese chips, which is what they claim."
9:23

SITUATION EXPLAINED: The Trump administration is closing the loophole that lets Chinese labs rent US chips. • A small group inside Commerce is building a slimmed-down replacement for the Biden-era AI Diffusion Rule, which the administration scrapped in 2025 • The target is Chinese AI firms accessing advanced chips remotely through data centers abroad, in countries including Thailand and Singapore • The current regime is upside down: the US now permits H200 sales to China while Beijing forbids its own firms from buying them • Nvidia's share of China's AI chip market fell from 95% in 2023 to 8% in 2026, while Huawei climbed to roughly 50-60% Theo Jaffee: "It's kind of unclear to me how important access to American chips is for Chinese AI progress, especially if GLM 5.3 is served entirely on Chinese chips, which is what they claim."

MTS

10,458 次观看 • 25 天前

$MRVL is where $MU was a year ago. Jordi Visser thinks Marvell has a chance to be a Micron-type name over the course of the next two years. To understand what that means, you have to understand what Micron was. A year ago, Visser wrote a paper on inference demand and the amount of memory that AI chips would need relative to GPUs. He flagged a handful of names, most of them are now up 4x-8x. $MU was the clearest expression of that thesis. When a generalist investor can identify the constraint and write a paper about it, it usually means there's still runway left. When everyone knows, the trade is over. Memory is now in that second phase. The DRAM ETF pulled in six billion dollars in a short window, one of the fastest ETF inflows in history. Retail is heavily involved so the near-term risk-reward has changed and Jordi has trimmed accordingly. $MRVL is where memory was a year ago. It only broke out two months ago. The optical interconnect thesis is less crowded and harder for competitors to replicate. Marvell is also directly tied to the Vera Rubin architecture that hasn't launched yet. The comparison to Micron is about the stage of the trade. Milk Road PRO has been in $MRVL since before the breakout. Start your $1 trial and see the full setup. Link in bio.
4:43

$MRVL is where $MU was a year ago. Jordi Visser thinks Marvell has a chance to be a Micron-type name over the course of the next two years. To understand what that means, you have to understand what Micron was. A year ago, Visser wrote a paper on inference demand and the amount of memory that AI chips would need relative to GPUs. He flagged a handful of names, most of them are now up 4x-8x. $MU was the clearest expression of that thesis. When a generalist investor can identify the constraint and write a paper about it, it usually means there's still runway left. When everyone knows, the trade is over. Memory is now in that second phase. The DRAM ETF pulled in six billion dollars in a short window, one of the fastest ETF inflows in history. Retail is heavily involved so the near-term risk-reward has changed and Jordi has trimmed accordingly. $MRVL is where memory was a year ago. It only broke out two months ago. The optical interconnect thesis is less crowded and harder for competitors to replicate. Marvell is also directly tied to the Vera Rubin architecture that hasn't launched yet. The comparison to Micron is about the stage of the trade. Milk Road PRO has been in $MRVL since before the breakout. Start your $1 trial and see the full setup. Link in bio.

Milk Road Stocks

37,727 次观看 • 1 个月前

🚨 WARNING: NVIDIA x ELON MUSK DEAL IS BUILT ON FAKE NUMBERS!! Michael Burry published an analysis calling the structure “Fugazi”, meaning fake. If the structure is real, we could be heading for a COLLAPSE: He is alleging that BILLIONS of dollars in Nvidia chips are being hidden off balance sheets, and that American retirees are unknowingly funding the whole thing. Nvidia, the world's largest AI chip company sold $5.4 BILLION worth of its most advanced GPUs, the GB200, to a company called Valor. Valor is not a real operating business. It is a special purpose vehicle, a shell company created specifically to hold these chips and nothing else. Nvidia also invested $1.9 BILLION of its own money directly into Valor on top of the sale. Those 100,000+ chips are now physically inside xAI's data center. xAI is Elon Musk's artificial intelligence company, the one that builds Grok. xAI is using every single one of those chips right now to run its AI models. But here is what Burry is flagging. Neither Nvidia nor xAI owns those chips on paper. Valor, the shell company holds legal title. That means $5.4 BILLION in GPU assets do not show up on Nvidia's balance sheet as inventory. They do not show up on xAI's balance sheet as assets. They are legally invisible to both companies. Nvidia gets to book the $5.4 BILLION as a completed sale and record it as revenue. xAI gets full use of the chips without owning them. And the risk disappears into a shell company in the middle. Now here is where American retirees enter the picture. Valor needed $3.5 BILLION in debt to fund this structure. Apollo provided it. Apollo is one of the largest asset managers on earth with $1.03 TRILLION under management and $834 BILLION specifically in private credit. Apollo raised the $3.5 BILLION, packaged it into debt securities, and sold those securities to Athene. Athene is Apollo's own insurance company. It sells fixed and indexed annuities, retirement savings products, to ordinary Americans. When a retiree buys an Athene annuity, they believe their money is sitting in safe, stable investments. That money is now inside a structure funding Elon Musk's AI data center. The numbers inside Athene are most alarming. Athene holds $74.2 BILLION in reserves. It has moved $217 BILLION in assets into a captive insurer based in Bermuda, meaning those assets sit outside normal US insurance regulation and oversight. Of the entire portfolio, 34.7%, equal to $103 BILLION, is classified as Level 3 assets. Level 3 is an accounting classification that means there is no observable market price for these assets. No outside party can independently verify what they are actually worth. The leverage sitting on top of those unpriced assets is 16 times. Burry's says: Every step of this structure is technically legal and publicly disclosed. But the entire thing was deliberately engineered across 8 to 12 steps to move credit risk off balance sheets and away from any market pricing. Nvidia books the revenue. Apollo collects the fees. xAI gets the computing power. And retirees sitting at the bottom of a 16x leveraged Bermuda insurance structure, holding $103 BILLION in assets with no market price carry the risk without knowing it exists. I’ve been in finance for more than 15 years. When I EXIT the markets completely, I’ll say it here publicly, like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:33

🚨 WARNING: NVIDIA x ELON MUSK DEAL IS BUILT ON FAKE NUMBERS!! Michael Burry published an analysis calling the structure “Fugazi”, meaning fake. If the structure is real, we could be heading for a COLLAPSE: He is alleging that BILLIONS of dollars in Nvidia chips are being hidden off balance sheets, and that American retirees are unknowingly funding the whole thing. Nvidia, the world's largest AI chip company sold $5.4 BILLION worth of its most advanced GPUs, the GB200, to a company called Valor. Valor is not a real operating business. It is a special purpose vehicle, a shell company created specifically to hold these chips and nothing else. Nvidia also invested $1.9 BILLION of its own money directly into Valor on top of the sale. Those 100,000+ chips are now physically inside xAI's data center. xAI is Elon Musk's artificial intelligence company, the one that builds Grok. xAI is using every single one of those chips right now to run its AI models. But here is what Burry is flagging. Neither Nvidia nor xAI owns those chips on paper. Valor, the shell company holds legal title. That means $5.4 BILLION in GPU assets do not show up on Nvidia's balance sheet as inventory. They do not show up on xAI's balance sheet as assets. They are legally invisible to both companies. Nvidia gets to book the $5.4 BILLION as a completed sale and record it as revenue. xAI gets full use of the chips without owning them. And the risk disappears into a shell company in the middle. Now here is where American retirees enter the picture. Valor needed $3.5 BILLION in debt to fund this structure. Apollo provided it. Apollo is one of the largest asset managers on earth with $1.03 TRILLION under management and $834 BILLION specifically in private credit. Apollo raised the $3.5 BILLION, packaged it into debt securities, and sold those securities to Athene. Athene is Apollo's own insurance company. It sells fixed and indexed annuities, retirement savings products, to ordinary Americans. When a retiree buys an Athene annuity, they believe their money is sitting in safe, stable investments. That money is now inside a structure funding Elon Musk's AI data center. The numbers inside Athene are most alarming. Athene holds $74.2 BILLION in reserves. It has moved $217 BILLION in assets into a captive insurer based in Bermuda, meaning those assets sit outside normal US insurance regulation and oversight. Of the entire portfolio, 34.7%, equal to $103 BILLION, is classified as Level 3 assets. Level 3 is an accounting classification that means there is no observable market price for these assets. No outside party can independently verify what they are actually worth. The leverage sitting on top of those unpriced assets is 16 times. Burry's says: Every step of this structure is technically legal and publicly disclosed. But the entire thing was deliberately engineered across 8 to 12 steps to move credit risk off balance sheets and away from any market pricing. Nvidia books the revenue. Apollo collects the fees. xAI gets the computing power. And retirees sitting at the bottom of a 16x leveraged Bermuda insurance structure, holding $103 BILLION in assets with no market price carry the risk without knowing it exists. I’ve been in finance for more than 15 years. When I EXIT the markets completely, I’ll say it here publicly, like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

WhaleTwits

48,759 次观看 • 3 个月前

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.
1:42

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.

Bull Theory

26,765 次观看 • 3 个月前

🚨 SOMETHING VERY STRANGE IS HAPPENING Anthropic will go public in November at a $2T valuation. The biggest IPO in market history. And Wall Street is already lining up the buyers before it happens. I've been trading for more than 15 years and have never seen them build demand for an IPO this aggressively: Anthropic is preparing to raise $100 BILLION. Nvidia is lining up as much as $10 BILLION as an anchor investor. Read that again: The company selling the chips powering the AI boom is about to become one of the biggest buyers of the AI company going public. Before the public even gets in. Why? Because Anthropic does not just create demand for Anthropic. It pulls liquidity from everywhere else: - Retail sells stocks to chase the IPO. - Funds raise cash for allocation. - Institutions rebalance portfolios. - Everyone wants exposure to the biggest AI deal in history. But here is where most people are looking at it wrong. They’re asking: WHAT WILL ANTHROPIC TAKE MONEY FROM? I’m asking: WHERE WILL ALL THAT MONEY GO NEXT? That capital funds more compute: More compute means more chips. More chips = more data centers. More data centers = more electricity, grid infrastructure and raw materials. The chain is simple: ANTHROPIC → CHIPS → DATA CENTERS → POWER → COPPER That is where the opportunity starts. The first phase of the AI boom was about the models: ChatGPT. Claude. Gemini. The next phase is about the physical infrastructure needed to keep them running. Electricity. Power grids. Semiconductors. Data centers. Cooling. Copper. I told you to buy copper months ago. We already locked in BIG profits. And that wasn’t random: AI does not run on prompts. It runs on physical infrastructure. Now look at Nvidia: AI companies spend billions buying Nvidia chips. Nvidia makes billions from that demand. Now Nvidia is preparing to put as much as $10 BILLION BACK into Anthropic. AI money → Nvidia → Anthropic → more compute → more infrastructure A $100B raise does not stop at Anthropic. It works its way through the entire AI supply chain. The easy AI trade was buying the obvious names. The next trade is finding the bottlenecks BEFORE everyone else realizes they are bottlenecks. That is what I’m looking for now. That is where the next opportunity will be. Remember, I’ve been trading markets for over 15 years. I’m already watching where this capital is moving next. When I find the next opportunity worth taking, I’ll post it here like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:38

🚨 SOMETHING VERY STRANGE IS HAPPENING Anthropic will go public in November at a $2T valuation. The biggest IPO in market history. And Wall Street is already lining up the buyers before it happens. I've been trading for more than 15 years and have never seen them build demand for an IPO this aggressively: Anthropic is preparing to raise $100 BILLION. Nvidia is lining up as much as $10 BILLION as an anchor investor. Read that again: The company selling the chips powering the AI boom is about to become one of the biggest buyers of the AI company going public. Before the public even gets in. Why? Because Anthropic does not just create demand for Anthropic. It pulls liquidity from everywhere else: - Retail sells stocks to chase the IPO. - Funds raise cash for allocation. - Institutions rebalance portfolios. - Everyone wants exposure to the biggest AI deal in history. But here is where most people are looking at it wrong. They’re asking: WHAT WILL ANTHROPIC TAKE MONEY FROM? I’m asking: WHERE WILL ALL THAT MONEY GO NEXT? That capital funds more compute: More compute means more chips. More chips = more data centers. More data centers = more electricity, grid infrastructure and raw materials. The chain is simple: ANTHROPIC → CHIPS → DATA CENTERS → POWER → COPPER That is where the opportunity starts. The first phase of the AI boom was about the models: ChatGPT. Claude. Gemini. The next phase is about the physical infrastructure needed to keep them running. Electricity. Power grids. Semiconductors. Data centers. Cooling. Copper. I told you to buy copper months ago. We already locked in BIG profits. And that wasn’t random: AI does not run on prompts. It runs on physical infrastructure. Now look at Nvidia: AI companies spend billions buying Nvidia chips. Nvidia makes billions from that demand. Now Nvidia is preparing to put as much as $10 BILLION BACK into Anthropic. AI money → Nvidia → Anthropic → more compute → more infrastructure A $100B raise does not stop at Anthropic. It works its way through the entire AI supply chain. The easy AI trade was buying the obvious names. The next trade is finding the bottlenecks BEFORE everyone else realizes they are bottlenecks. That is what I’m looking for now. That is where the next opportunity will be. Remember, I’ve been trading markets for over 15 years. I’m already watching where this capital is moving next. When I find the next opportunity worth taking, I’ll post it here like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

Alex Mason 👁△

108,239 次观看 • 2 天前

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!
1:04

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!

Milk Road AI

28,622 次观看 • 4 个月前

#WATCH | 🤖From AI chips to dance moves 🕺Nvidia CEO Jensen Huang breaks into a dance at an employee celebration event in Taipei, offering a rare glimpse of his lighter side. Nvidia's CEO said on May 27 that the chip company plans to invest around $150 billion a year in Taiwan, terming it the "epicentre" of the AI revolution and predicting it will be the world's tech manufacturing hub for a long time. Source: Mario Nawfal #JensenHuang #Taiwan #Nividia
1:07

#WATCH | 🤖From AI chips to dance moves 🕺Nvidia CEO Jensen Huang breaks into a dance at an employee celebration event in Taipei, offering a rare glimpse of his lighter side. Nvidia's CEO said on May 27 that the chip company plans to invest around $150 billion a year in Taiwan, terming it the "epicentre" of the AI revolution and predicting it will be the world's tech manufacturing hub for a long time. Source: Mario Nawfal #JensenHuang #Taiwan #Nividia

Moneycontrol

10,299 次观看 • 3 个月前

Sam Altman: "Very soon an AI will just be running for you in the background all the time" That entire model is on its way out. But the vision isn't the tell. This is: He says everyone's going to want "so much more of this AI infrastructure than we think." That's not a roadmap. That's a man admitting he can't build chips and power fast enough. The always-on assistant is the headline. The infrastructure bottleneck underneath it is the trade almost nobody is pricing in. That gap is the whole edge.
0:25

Sam Altman: "Very soon an AI will just be running for you in the background all the time" That entire model is on its way out. But the vision isn't the tell. This is: He says everyone's going to want "so much more of this AI infrastructure than we think." That's not a roadmap. That's a man admitting he can't build chips and power fast enough. The always-on assistant is the headline. The infrastructure bottleneck underneath it is the trade almost nobody is pricing in. That gap is the whole edge.

plutos

25,689 次观看 • 3 个月前

Jensen Huang just revealed his $500 BILLION financing scheme with 6 of the biggest money managers on Earth. Here's the deal they signed: Nvidia and those 6 firms are mobilizing more than $500 billion of outside capital to finance AI infrastructure. The money goes to Nvidia's own customers so they can build data centers and buy Nvidia chips. Nvidia is not putting up a single dollar. The CHIPS themselves become the collateral. This is like "buying a GM car and getting the financing from GM." Jensen clarified that none of it is Nvidia's money. That part is technically true. But then Larry Fink explained what they are actually building... Fink runs BlackRock, the largest asset manager on Earth. Asked about financing data centers, he said this is the very beginning, the way it was when he started out in the mortgage backed securities market in the 1970s. Then he called it the "next frontier of financial engineering." He compared the AI buildout to the machine that CRASHED the world economy in 2008. Then KKR's global head of digital infrastructure laid out the mechanics: He said the revenue coming off those chips can be securitized, the risk divided up, and the slices sold to investors who want exposure anywhere in the stack. So the GPU is the house, the compute bill is the mortgage payment, and the slices get sold to whoever wants them. Blackstone's John Gray made the comparison himself. He said when you buy a house the bank underwrites you and also looks at the value of the home. When an airline buys a plane, they look at the credit of the airline and at the plane. The chips are the plane. But a plane holds its value because Boeing cannot make your plane obsolete on purpose. Nvidia can, and it does it roughly every year. KKR's own man spent part of that panel praising Vera Rubin, calling the jump in tokens per watt a step change. Every leap like that makes the previous generation worth less. And the price per token has already fallen 99%, so the collateral behind these loans depreciates on a schedule Nvidia controls, while the revenue those chips earn keeps getting cheaper. So what happens if the AI companies run out of cash? Jensen said somebody else can take the machines over and operate them. "There will always be a customer for that computing platform." He planned the repossession before the first loan even closed. Then who ends up holding this paper? Fink answered that one himself: He said they will be working with pension funds across the world. David Solomon pointed at the $9 trillion sitting in US money market funds. Fink pitched moving that cash into longer dated returns and said investors who are overweight equities will rotate in too. And $500 billion is only the opening bid. Fink said the US alone needs over 70 gigawatts of power for this, and every gigawatt costs $50 to $60 billion to build. That works out to more than $3.5 TRILLION for America by itself. Six days earlier, Apollo had called its $35 billion Broadcom financing the largest of its kind ever done. This deal is literally more than 14x bigger. Solomon was the only guy on that panel who mentioned the risk. He admitted the returns will NOT all be ample, that capital will get allocated to things that do not work, and that there will be winners and losers. Jensen's case is that the machines print money. He said AI tokens are incredibly profitable. Within months everyone will realize the AI labs are extremely profitable, and that when those labs go public it will be the biggest IPOs in history. None of that has happened yet. The financing is being built right now, ahead of the proof. So this is either the largest infrastructure buildout in history, or Wall Street just turned the AI bubble into bonds and handed them to the pension funds.
0:42

Jensen Huang just revealed his $500 BILLION financing scheme with 6 of the biggest money managers on Earth. Here's the deal they signed: Nvidia and those 6 firms are mobilizing more than $500 billion of outside capital to finance AI infrastructure. The money goes to Nvidia's own customers so they can build data centers and buy Nvidia chips. Nvidia is not putting up a single dollar. The CHIPS themselves become the collateral. This is like "buying a GM car and getting the financing from GM." Jensen clarified that none of it is Nvidia's money. That part is technically true. But then Larry Fink explained what they are actually building... Fink runs BlackRock, the largest asset manager on Earth. Asked about financing data centers, he said this is the very beginning, the way it was when he started out in the mortgage backed securities market in the 1970s. Then he called it the "next frontier of financial engineering." He compared the AI buildout to the machine that CRASHED the world economy in 2008. Then KKR's global head of digital infrastructure laid out the mechanics: He said the revenue coming off those chips can be securitized, the risk divided up, and the slices sold to investors who want exposure anywhere in the stack. So the GPU is the house, the compute bill is the mortgage payment, and the slices get sold to whoever wants them. Blackstone's John Gray made the comparison himself. He said when you buy a house the bank underwrites you and also looks at the value of the home. When an airline buys a plane, they look at the credit of the airline and at the plane. The chips are the plane. But a plane holds its value because Boeing cannot make your plane obsolete on purpose. Nvidia can, and it does it roughly every year. KKR's own man spent part of that panel praising Vera Rubin, calling the jump in tokens per watt a step change. Every leap like that makes the previous generation worth less. And the price per token has already fallen 99%, so the collateral behind these loans depreciates on a schedule Nvidia controls, while the revenue those chips earn keeps getting cheaper. So what happens if the AI companies run out of cash? Jensen said somebody else can take the machines over and operate them. "There will always be a customer for that computing platform." He planned the repossession before the first loan even closed. Then who ends up holding this paper? Fink answered that one himself: He said they will be working with pension funds across the world. David Solomon pointed at the $9 trillion sitting in US money market funds. Fink pitched moving that cash into longer dated returns and said investors who are overweight equities will rotate in too. And $500 billion is only the opening bid. Fink said the US alone needs over 70 gigawatts of power for this, and every gigawatt costs $50 to $60 billion to build. That works out to more than $3.5 TRILLION for America by itself. Six days earlier, Apollo had called its $35 billion Broadcom financing the largest of its kind ever done. This deal is literally more than 14x bigger. Solomon was the only guy on that panel who mentioned the risk. He admitted the returns will NOT all be ample, that capital will get allocated to things that do not work, and that there will be winners and losers. Jensen's case is that the machines print money. He said AI tokens are incredibly profitable. Within months everyone will realize the AI labs are extremely profitable, and that when those labs go public it will be the biggest IPOs in history. None of that has happened yet. The financing is being built right now, ahead of the proof. So this is either the largest infrastructure buildout in history, or Wall Street just turned the AI bubble into bonds and handed them to the pension funds.

Ricardo

33,839 次观看 • 1 个月前

The next two months put $IREN's entire pivot on one milestone. Horizon 1, its first GB300 super cluster at Childress, is targeted to hand off to $MSFT in Q3, roughly July through September, the opening delivery under the five year, $9.7 billion Microsoft contract and the point where that contract starts turning into revenue. For a year the company has been buying the hard things. Power, land, financing. Now it has to turn them into delivered compute. That is the whole test. "The world is structurally short compute, and the bottleneck is delivered data center and GPU capacity," said Daniel Roberts, Co-Founder and Co-CEO of $IREN. Horizon 1 is him putting that to the proof. Capacity nobody can energize is worthless. Capacity handed to a hyperscaler on schedule is the business. The rest of the window fills in around it. The Mirantis acquisition, signed in May, is pending close and adds the software layer to run the fleet. $3.1 billion of ARR sits under contract against a $4.4 billion target, so there is room for another customer, and management is openly chasing one. The $3.65 billion GPU financing that closed June 1 already funds most of the $MSFT hardware, with $NVDA and $DELL on the supply side. Sweetwater 1, energized in May, keeps ramping power behind all of it. The full year FY2026 results that put real numbers on all of this land just past the window, late August into September. The catalysts here are operational first, reported second. What makes that timeline credible is the record behind it. $IREN hit 50 EH/s on the schedule it set, energized Sweetwater 1 on schedule, and has Horizon 1-4 tracking for year-end. This is a team that keeps turning secured power into online capacity on time, and each build makes the next one faster. How many names in this AI buildout are actually delivering capacity on schedule, not just announcing it? It's not a sprint, it's a marathon.
0:31

The next two months put $IREN's entire pivot on one milestone. Horizon 1, its first GB300 super cluster at Childress, is targeted to hand off to $MSFT in Q3, roughly July through September, the opening delivery under the five year, $9.7 billion Microsoft contract and the point where that contract starts turning into revenue. For a year the company has been buying the hard things. Power, land, financing. Now it has to turn them into delivered compute. That is the whole test. "The world is structurally short compute, and the bottleneck is delivered data center and GPU capacity," said Daniel Roberts, Co-Founder and Co-CEO of $IREN. Horizon 1 is him putting that to the proof. Capacity nobody can energize is worthless. Capacity handed to a hyperscaler on schedule is the business. The rest of the window fills in around it. The Mirantis acquisition, signed in May, is pending close and adds the software layer to run the fleet. $3.1 billion of ARR sits under contract against a $4.4 billion target, so there is room for another customer, and management is openly chasing one. The $3.65 billion GPU financing that closed June 1 already funds most of the $MSFT hardware, with $NVDA and $DELL on the supply side. Sweetwater 1, energized in May, keeps ramping power behind all of it. The full year FY2026 results that put real numbers on all of this land just past the window, late August into September. The catalysts here are operational first, reported second. What makes that timeline credible is the record behind it. $IREN hit 50 EH/s on the schedule it set, energized Sweetwater 1 on schedule, and has Horizon 1-4 tracking for year-end. This is a team that keeps turning secured power into online capacity on time, and each build makes the next one faster. How many names in this AI buildout are actually delivering capacity on schedule, not just announcing it? It's not a sprint, it's a marathon.

Patient Investor

105,170 次观看 • 3 个月前