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Melvin

@MelvinInvests • 24,656 subscribers

AI Analyst @MilkRoadAI | Finding opportunities across AI, photonics, defense, space, and tech.

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This is your friendly reminder to take some profits here! Days like this, when your positions are making strong moves higher, are exactly when it makes sense to look at what has gotten too big in your portfolio. I took some profits today to lock in part of those gains and free up money for other opportunities. Nobody ever lost money taking a profit. You do not need to sell at the exact top for taking some money off the table to be a good decision. You can remain bullish on a company and still trim your position, especially when it has grown large enough to control how your entire portfolio performs. I still think we are going higher but I expect volatility to pick up again especially as we get closer to the Fed meeting. Expectations around rates can change quickly and a single inflation report or comment from the Fed can shift sentiment. That is why I like having some cash available. When everything is green, nobody wants to sell then a pullback happens and suddenly everyone wishes they had money to buy. Taking some profits into strength gives me flexibility to add when opportunities show up. My broader AI thesis remains intact. I still believe demand for AI compute remains strong, with plenty of opportunity across the memory, networking, and power needed to support that buildout. These are investments that play out over years, and a few volatile trading sessions do not automatically change that outlook. But strong businesses can still have sharp pullbacks, especially when their stocks have already priced in a lot of growth. Being bullish on the next few years does not mean I expect a straight line higher over the next few weeks. For me, the balance is staying invested in the companies I believe in, trimming positions that have gotten too large and keeping some cash ready for opportunities. Taking a little off helps me stay comfortable holding the rest through volatility. I do not need to capture every last dollar of upside to have a great return.

This is your friendly reminder to take some profits here! Days like this, when your positions are making strong moves higher, are exactly when it makes sense to look at what has gotten too big in your portfolio. I took some profits today to lock in part of those gains and free up money for other opportunities. Nobody ever lost money taking a profit. You do not need to sell at the exact top for taking some money off the table to be a good decision. You can remain bullish on a company and still trim your position, especially when it has grown large enough to control how your entire portfolio performs. I still think we are going higher but I expect volatility to pick up again especially as we get closer to the Fed meeting. Expectations around rates can change quickly and a single inflation report or comment from the Fed can shift sentiment. That is why I like having some cash available. When everything is green, nobody wants to sell then a pullback happens and suddenly everyone wishes they had money to buy. Taking some profits into strength gives me flexibility to add when opportunities show up. My broader AI thesis remains intact. I still believe demand for AI compute remains strong, with plenty of opportunity across the memory, networking, and power needed to support that buildout. These are investments that play out over years, and a few volatile trading sessions do not automatically change that outlook. But strong businesses can still have sharp pullbacks, especially when their stocks have already priced in a lot of growth. Being bullish on the next few years does not mean I expect a straight line higher over the next few weeks. For me, the balance is staying invested in the companies I believe in, trimming positions that have gotten too large and keeping some cash ready for opportunities. Taking a little off helps me stay comfortable holding the rest through volatility. I do not need to capture every last dollar of upside to have a great return.

12,674 views

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

180,578 views

Why is the market selling off today? (Save this). Today's selloff is bigger and messier than what we've seen lately, KOSPI crashed almost 11% overnight, chip stocks are getting hit everywhere and it's not because AI demand suddenly disappeared but rather a bunch of fears piling up at once that I think are getting way overplayed. Start with the AI ROI thing since it's been building since last week's earnings. Tesla and Alphabet both kicked off earnings season with big capex numbers and negative free cash flow and even with strong revenue growth both stocks got hammered. That set the tone of we don't care if capex is growing, show us the cash, and it's carrying into this week with Amazon, Meta, Microsoft and Apple all reporting, which isn't helping the nerves. But look at what actually happened with Alphabet, cloud revenue grew 81%, total sales grew 24%, that's not a company torching cash on nothing, that's a company scaling into demand it can barely keep up with. Negative free cash flow during a capex supercycle is normal, you build the data centers and buy the GPUs before the revenue shows up. Judging a buildout phase like it's a mature business is the wrong lens, and that's basically what happened last week and what's still happening today. Then there's China chip competition, which is honestly the biggest accelerant of today's move. CXMT's IPO shares rose over 466% and combined with headlines about China's homegrown DUV lithography progress, it triggered a brutal rout in Korean chipmakers, Samsung fell as much as 13%, SK Hynix over 14%, Kioxia nearly 18%, dragging the KOSPI down almost 11% and into an eighth circuit breaker this year. That spilled straight into Nvidia, ASML, Sandisk and Seagate here in the US, with Nasdaq 100 futures down over 1% before the bell. But here's the thing, five DUV units this year against ASML's 131 a year, running performance closer to a 2008 design, is not an equipment moat collapsing, it's a headline that's gotten repeated so much this week it's built its own gravity. These tools are aimed at mature nodes like automotive and industrial chips, not the leading edge logic or HBM that actually drives the AI trade, so the read through to Nvidia, ASML or Applied Materials earnings power is basically nothing. The CXMT pop is scarcity, people bidding up the only pure play China memory stock they can get their hands on, not a sign that oversupply is coming. And Korean chipmakers dropping 12 to 14% in one session looks a lot more like leverage unwinding after a parabolic run than a real rethink of Samsung or SK Hynix's HBM backlog, which both companies have already said is basically sold out for the year. Geopolitics is actually the one spot where the news should be helping, not hurting. US and Iran hostilities seem to have paused for now, which should be easing oil driven inflation fears. If this were purely a geopolitical panic you'd expect oil spiking and yields following, but that's not what's happening, this move is chip specific and Asia led, not an oil shock like a week or two ago. Rates and the Fed are still in play, decision lands tomorrow, and people are nervous about higher for longer language even though a hike isn't the base case. On top of that, reports that Nvidia's five year credit default swap costs jumped by a record margin are getting read by some as a credit risk signal tied to all this AI debt spending. But a one day CDS spike during a market wide panic is a fear indicator, not proof of an actual credit problem, spreads on every big name widen fast when volatility spikes, Nvidia's balance sheet hasn't changed in the last 24 hours. Fed futures are pricing in essentially no chance of a surprise hike tomorrow, this is a hold meeting, and I'd bet the hawkish jitters fade fast once Warsh actually talks. Then there's the bigger liquidity and positioning story, which I think explains more of today's violence than any single headline. KOSPI is down nearly 29% for the month now, steeper than 2008, mostly because Korean chipmakers had turned into crypto like gambling tokens, running way too far, too fast on retail leverage and margin debt, and now unwinding just as hard on the way down. That's positioning excess getting flushed, not HBM demand disappearing or hyperscalers pulling back. Nothing in the actual order books, capex guidance or HBM contract pricing has changed, DRAM and NAND prices are still climbing quarter over quarter, nobody's canceled a GPU order or a data center project. What changed is how much leverage was sitting on top of this trade, and that's getting ripped out in one ugly session. This is one of the scariest looking selloffs we've had all year but scary looking and actually broken are two different things. Every headline driving today, the China lithography story, the CXMT IPO, the Nvidia CDS spike, the Fed jitters, looks a lot less scary once you dig into the actual numbers, and none of it touches real AI infrastructure demand or supply. This looks like leverage and sentiment unwinding, not the long term thesis breaking. If you want to see exactly what I'm buying into this, join Milk Road Pro for just $1 using the link below.

Why is the market selling off today? (Save this). Today's selloff is bigger and messier than what we've seen lately, KOSPI crashed almost 11% overnight, chip stocks are getting hit everywhere and it's not because AI demand suddenly disappeared but rather a bunch of fears piling up at once that I think are getting way overplayed. Start with the AI ROI thing since it's been building since last week's earnings. Tesla and Alphabet both kicked off earnings season with big capex numbers and negative free cash flow and even with strong revenue growth both stocks got hammered. That set the tone of we don't care if capex is growing, show us the cash, and it's carrying into this week with Amazon, Meta, Microsoft and Apple all reporting, which isn't helping the nerves. But look at what actually happened with Alphabet, cloud revenue grew 81%, total sales grew 24%, that's not a company torching cash on nothing, that's a company scaling into demand it can barely keep up with. Negative free cash flow during a capex supercycle is normal, you build the data centers and buy the GPUs before the revenue shows up. Judging a buildout phase like it's a mature business is the wrong lens, and that's basically what happened last week and what's still happening today. Then there's China chip competition, which is honestly the biggest accelerant of today's move. CXMT's IPO shares rose over 466% and combined with headlines about China's homegrown DUV lithography progress, it triggered a brutal rout in Korean chipmakers, Samsung fell as much as 13%, SK Hynix over 14%, Kioxia nearly 18%, dragging the KOSPI down almost 11% and into an eighth circuit breaker this year. That spilled straight into Nvidia, ASML, Sandisk and Seagate here in the US, with Nasdaq 100 futures down over 1% before the bell. But here's the thing, five DUV units this year against ASML's 131 a year, running performance closer to a 2008 design, is not an equipment moat collapsing, it's a headline that's gotten repeated so much this week it's built its own gravity. These tools are aimed at mature nodes like automotive and industrial chips, not the leading edge logic or HBM that actually drives the AI trade, so the read through to Nvidia, ASML or Applied Materials earnings power is basically nothing. The CXMT pop is scarcity, people bidding up the only pure play China memory stock they can get their hands on, not a sign that oversupply is coming. And Korean chipmakers dropping 12 to 14% in one session looks a lot more like leverage unwinding after a parabolic run than a real rethink of Samsung or SK Hynix's HBM backlog, which both companies have already said is basically sold out for the year. Geopolitics is actually the one spot where the news should be helping, not hurting. US and Iran hostilities seem to have paused for now, which should be easing oil driven inflation fears. If this were purely a geopolitical panic you'd expect oil spiking and yields following, but that's not what's happening, this move is chip specific and Asia led, not an oil shock like a week or two ago. Rates and the Fed are still in play, decision lands tomorrow, and people are nervous about higher for longer language even though a hike isn't the base case. On top of that, reports that Nvidia's five year credit default swap costs jumped by a record margin are getting read by some as a credit risk signal tied to all this AI debt spending. But a one day CDS spike during a market wide panic is a fear indicator, not proof of an actual credit problem, spreads on every big name widen fast when volatility spikes, Nvidia's balance sheet hasn't changed in the last 24 hours. Fed futures are pricing in essentially no chance of a surprise hike tomorrow, this is a hold meeting, and I'd bet the hawkish jitters fade fast once Warsh actually talks. Then there's the bigger liquidity and positioning story, which I think explains more of today's violence than any single headline. KOSPI is down nearly 29% for the month now, steeper than 2008, mostly because Korean chipmakers had turned into crypto like gambling tokens, running way too far, too fast on retail leverage and margin debt, and now unwinding just as hard on the way down. That's positioning excess getting flushed, not HBM demand disappearing or hyperscalers pulling back. Nothing in the actual order books, capex guidance or HBM contract pricing has changed, DRAM and NAND prices are still climbing quarter over quarter, nobody's canceled a GPU order or a data center project. What changed is how much leverage was sitting on top of this trade, and that's getting ripped out in one ugly session. This is one of the scariest looking selloffs we've had all year but scary looking and actually broken are two different things. Every headline driving today, the China lithography story, the CXMT IPO, the Nvidia CDS spike, the Fed jitters, looks a lot less scary once you dig into the actual numbers, and none of it touches real AI infrastructure demand or supply. This looks like leverage and sentiment unwinding, not the long term thesis breaking. If you want to see exactly what I'm buying into this, join Milk Road Pro for just $1 using the link below.

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Robotics is following AI’s exact playbook and the Capex explosion is coming next. The first step is already happening, money is flooding into the companies building physical AI. Robotics and physical AI startups raised about $16.3 billion across 492 deals in the first quarter of 2026, roughly 4.5 times the average quarterly funding from 2021–2025. In the first half of 2026, physical AI companies raised $47.4 billion, more than the sector raised across all of 2022–2024 combined. That is how the AI cycle started, venture capital funded the technology first, then companies began spending hundreds of billions on the infrastructure needed to deploy it. Robotics is now moving from research labs into warehouses, factories, auto plants, logistics centers, and defense systems. Goldman Sachs raised its 2035 humanoid robot forecast from 1.38 million units to 6.48 million, with the market potentially reaching $138 billion. The reason is falling costs and Goldman expects average robot prices to decline from about $41,800 in 2025 to $21,300 by 2035. As prices fall, the payback period could shrink from 2.8 years in 2026 to about 1.9 years in 2027, making robots much easier for companies to justify as capital investments. That is when robotics can trigger its own capex cycle. Companies will spend not just on robots but also on factories, sensors, chips, batteries, software, power systems, data centers, and new automated facilities. Amazon is already a major example because its automation program could save roughly $72 billion between 2026 and 2030 and add about 240 basis points to operating margins. Morgan Stanley sees the long term opportunity as even larger, estimating 1 billion humanoid robots and about $7.5 trillion in annual revenue by 2050. And the biggest beneficiaries in all of this will be the picks and shovels companies behind the robots. That includes Nvidia and Renesas for chips, Teradyne for automation, Harmonic Drive for precision gearboxes, and Toyota, Honda, JTEKT, Aisin, and MinebeaMitsumi for manufacturing and motion control components. Bullish on robotics and the picks and shovels behind the next capex cycle and If you enjoyed reading this, make sure to follow Melvin for more robotics and AI insights. If you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can join for just $1 using the link below.
3:02
MelvinInvests's profile picture

Robotics is following AI’s exact playbook and the Capex explosion is coming next. The first step is already happening, money is flooding into the companies building physical AI. Robotics and physical AI startups raised about $16.3 billion across 492 deals in the first quarter of 2026, roughly 4.5 times the average quarterly funding from 2021–2025. In the first half of 2026, physical AI companies raised $47.4 billion, more than the sector raised across all of 2022–2024 combined. That is how the AI cycle started, venture capital funded the technology first, then companies began spending hundreds of billions on the infrastructure needed to deploy it. Robotics is now moving from research labs into warehouses, factories, auto plants, logistics centers, and defense systems. Goldman Sachs raised its 2035 humanoid robot forecast from 1.38 million units to 6.48 million, with the market potentially reaching $138 billion. The reason is falling costs and Goldman expects average robot prices to decline from about $41,800 in 2025 to $21,300 by 2035. As prices fall, the payback period could shrink from 2.8 years in 2026 to about 1.9 years in 2027, making robots much easier for companies to justify as capital investments. That is when robotics can trigger its own capex cycle. Companies will spend not just on robots but also on factories, sensors, chips, batteries, software, power systems, data centers, and new automated facilities. Amazon is already a major example because its automation program could save roughly $72 billion between 2026 and 2030 and add about 240 basis points to operating margins. Morgan Stanley sees the long term opportunity as even larger, estimating 1 billion humanoid robots and about $7.5 trillion in annual revenue by 2050. And the biggest beneficiaries in all of this will be the picks and shovels companies behind the robots. That includes Nvidia and Renesas for chips, Teradyne for automation, Harmonic Drive for precision gearboxes, and Toyota, Honda, JTEKT, Aisin, and MinebeaMitsumi for manufacturing and motion control components. Bullish on robotics and the picks and shovels behind the next capex cycle and If you enjoyed reading this, make sure to follow Melvin for more robotics and AI insights. If you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can join for just $1 using the link below.

Melvin

22,786 views • 1 day ago

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
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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

152,406 views • 2 months ago

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

Melvin

131,391 views • 2 months ago

Dylan Patel just mapped out the most important investment theme in AI infrastructure (Save this). "In about two years, solar plus battery will be cheaper than gas." Every new NVIDIA Blackwell rack pulls 120 kilowatts, Rubin Ultra rack pulls 600 kilowatts and the next generation hits a megawatt. The US grid cannot keep up, interconnection queues now run five years in many markets so the entire industry is being forced to solve power from first principles. The solar thesis is already happening. BloombergNEF's 2026 LCOE report, covering 800+ financed projects across 50+ markets puts solar plus 4 hour battery storage at $57 per megawatt-hour. Combined cycle gas turbines hit $102 per megawatt hour, the highest on record, up 16% year over year. In California and parts of Texas, solar plus storage is already cheaper than gas for data center power today and solar panel costs are expected to drop another 30% by 2035. Getting power from the grid into the form chips actually require is an entire industry unto itself and NVIDIA just rewrote the rules. The 800 volt DC transition is the most important infrastructure shift that's happening right now. Today's data centers run on 48 volt DC power delivery, a single next-generation GPU pulls over 2,500 watts and at 48 volts, the current required to power a megawatt rack would melt the copper wiring. The investment thesis breaks into four layers and the first layer is power semiconductors, specifically silicon carbide and gallium nitride. At 800 volts, traditional silicon based IGBTs hit their physical limits. SiC and GaN devices are the mandatory replacement. Infineon estimates $175,000 of semiconductor content per megawatt of AI rack power, versus almost nothing today and by 2030, power semiconductor content per AI cabinet grows from $15,000 to $115,000+. The names here are Infineon ($IFNNY), ON Semiconductor ($ON), Wolfspeed ($WOLF), Navitas ($NVTS), and STMicroelectronics ($STM). The second layer is power management and conversion. Vertiv ($VRT) is NVIDIA's lead architectural collaborator for the 800V transition, building the hardware that converts grid AC to 800V DC and the DC to DC power shelves for ultra dense racks. Eaton ($ETN) and Monolithic Power Systems ($MPWR) round out this layer. The third layer is grid to site infrastructure, GE Vernova ($GEV) builds the heavy electrical equipment that connects utility power to the data center campus. Orders are running at twice the rate of shipments, the classic leading indicator of sustained multi year revenue growth. The fourth layer is behind the meter power generation like your bloom energy because grid interconnection queues run five years, hyperscalers are bypassing the grid entirely, building dedicated gas, solar and battery systems on site. Make sure to follow me Melvin for more opportunities across the AI supply chain.
5:06
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Dylan Patel just mapped out the most important investment theme in AI infrastructure (Save this). "In about two years, solar plus battery will be cheaper than gas." Every new NVIDIA Blackwell rack pulls 120 kilowatts, Rubin Ultra rack pulls 600 kilowatts and the next generation hits a megawatt. The US grid cannot keep up, interconnection queues now run five years in many markets so the entire industry is being forced to solve power from first principles. The solar thesis is already happening. BloombergNEF's 2026 LCOE report, covering 800+ financed projects across 50+ markets puts solar plus 4 hour battery storage at $57 per megawatt-hour. Combined cycle gas turbines hit $102 per megawatt hour, the highest on record, up 16% year over year. In California and parts of Texas, solar plus storage is already cheaper than gas for data center power today and solar panel costs are expected to drop another 30% by 2035. Getting power from the grid into the form chips actually require is an entire industry unto itself and NVIDIA just rewrote the rules. The 800 volt DC transition is the most important infrastructure shift that's happening right now. Today's data centers run on 48 volt DC power delivery, a single next-generation GPU pulls over 2,500 watts and at 48 volts, the current required to power a megawatt rack would melt the copper wiring. The investment thesis breaks into four layers and the first layer is power semiconductors, specifically silicon carbide and gallium nitride. At 800 volts, traditional silicon based IGBTs hit their physical limits. SiC and GaN devices are the mandatory replacement. Infineon estimates $175,000 of semiconductor content per megawatt of AI rack power, versus almost nothing today and by 2030, power semiconductor content per AI cabinet grows from $15,000 to $115,000+. The names here are Infineon ($IFNNY), ON Semiconductor ($ON), Wolfspeed ($WOLF), Navitas ($NVTS), and STMicroelectronics ($STM). The second layer is power management and conversion. Vertiv ($VRT) is NVIDIA's lead architectural collaborator for the 800V transition, building the hardware that converts grid AC to 800V DC and the DC to DC power shelves for ultra dense racks. Eaton ($ETN) and Monolithic Power Systems ($MPWR) round out this layer. The third layer is grid to site infrastructure, GE Vernova ($GEV) builds the heavy electrical equipment that connects utility power to the data center campus. Orders are running at twice the rate of shipments, the classic leading indicator of sustained multi year revenue growth. The fourth layer is behind the meter power generation like your bloom energy because grid interconnection queues run five years, hyperscalers are bypassing the grid entirely, building dedicated gas, solar and battery systems on site. Make sure to follow me Melvin for more opportunities across the AI supply chain.

Melvin

107,952 views • 2 months ago

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

Melvin

93,835 views • 2 months ago

The majority of neoclouds will eventually go out of business but here is the winning formula if you want to win. (Save this). The core problem for the industry is that the economics of running GPU infrastructure only work at massive scale, with cheap financing and investment grade customers backing long term contracts. A lot of the names crowding the middle column of that chart are Bitcoin miners who converted their rigs into GPU racks chasing the AI trend, rather than companies built from the ground up for this business, which is exactly the kind of opportunistic entrant that gets wiped out when capital tightens or utilization dips. Nebius sits in the Neocloud Giants tier alongside CoreWeave, Lambda and Crusoe, and today's Q2 2026 print showed exactly why it's pulling away from the pack rather than getting lumped in with the 78 emerging players facing consolidation risk. Revenue hit $582 million, up 454% year over year, with annualized recurring revenue reaching $3.0 billion by the end of June, up 58% quarter over quarter. The company won four separate customer agreements each worth over $1 billion in total contract value and total contract value won during the quarter jumped 4x versus the prior period, a growth rate most of the smaller neoclouds on that chart simply can't match without hyperscaler grade balance sheets. Here's the vertical stack that sets Nebius apart from most names on that chart. Unlike pure GPU rental shops that lease space in someone else's data center, Nebius designs its own data centers, builds its own server racks and motherboards, procures its own compute, and runs a proprietary AI specific cloud platform layer on top of all of it. That full stack control, from silicon to software, is precisely what most of the emerging neoclouds in the chart's middle column lack, since converting a Bitcoin mining facility gives you power and cooling, but not in house rack engineering or a purpose built cloud software layer. Nebius has also been shifting from leased to owned infrastructure, with more than 75% of its contracted power now sitting at facilities it directly controls, up sharply from a mostly leased model just a year ago. That ownership shift is the difference between capturing margin over the long run versus being at the mercy of a landlord's lease terms, which is a structural advantage over neoclouds still renting third party space. Now for the pricing power piece. Nebius disclosed today that it's now charging $40-50 million per megawatt on new capacity deals, already signing its first one at that price this week, up from roughly $12 million per megawatt on its 2026 base contracts. Management also said it could sell its entire 2027 capacity right now on these terms but is deliberately holding some back for near term customer needs, a level of pricing leverage that tells you demand is outstripping supply for anyone offering real scale and reliability, exactly the customer profile the smaller, undercapitalized neoclouds struggle to attract. Nebius isn't a single product company either, which matters given how many names on that chart have no other legs to stand on if GPU rental margins compress. The company owns Avride, an autonomous driving and delivery robotics business with partnerships with Uber and Hyundai, TripleTen, a tech re skilling edtech platform and holds equity stakes in ClickHouse, the database company it spun out and recently backed in a funding round, and in Toloka, an AI data-labeling platform that sold a majority stake to Bezos Expeditions and Shopify in 2025. Those side businesses give Nebius optionality and diversified cash flow that a converted mining rig operator simply doesn't have. Bullish on Nebius, make sure to follow Melvin for more AI infrastructure insights, and if you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can check out the link below for more.
4:32
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The majority of neoclouds will eventually go out of business but here is the winning formula if you want to win. (Save this). The core problem for the industry is that the economics of running GPU infrastructure only work at massive scale, with cheap financing and investment grade customers backing long term contracts. A lot of the names crowding the middle column of that chart are Bitcoin miners who converted their rigs into GPU racks chasing the AI trend, rather than companies built from the ground up for this business, which is exactly the kind of opportunistic entrant that gets wiped out when capital tightens or utilization dips. Nebius sits in the Neocloud Giants tier alongside CoreWeave, Lambda and Crusoe, and today's Q2 2026 print showed exactly why it's pulling away from the pack rather than getting lumped in with the 78 emerging players facing consolidation risk. Revenue hit $582 million, up 454% year over year, with annualized recurring revenue reaching $3.0 billion by the end of June, up 58% quarter over quarter. The company won four separate customer agreements each worth over $1 billion in total contract value and total contract value won during the quarter jumped 4x versus the prior period, a growth rate most of the smaller neoclouds on that chart simply can't match without hyperscaler grade balance sheets. Here's the vertical stack that sets Nebius apart from most names on that chart. Unlike pure GPU rental shops that lease space in someone else's data center, Nebius designs its own data centers, builds its own server racks and motherboards, procures its own compute, and runs a proprietary AI specific cloud platform layer on top of all of it. That full stack control, from silicon to software, is precisely what most of the emerging neoclouds in the chart's middle column lack, since converting a Bitcoin mining facility gives you power and cooling, but not in house rack engineering or a purpose built cloud software layer. Nebius has also been shifting from leased to owned infrastructure, with more than 75% of its contracted power now sitting at facilities it directly controls, up sharply from a mostly leased model just a year ago. That ownership shift is the difference between capturing margin over the long run versus being at the mercy of a landlord's lease terms, which is a structural advantage over neoclouds still renting third party space. Now for the pricing power piece. Nebius disclosed today that it's now charging $40-50 million per megawatt on new capacity deals, already signing its first one at that price this week, up from roughly $12 million per megawatt on its 2026 base contracts. Management also said it could sell its entire 2027 capacity right now on these terms but is deliberately holding some back for near term customer needs, a level of pricing leverage that tells you demand is outstripping supply for anyone offering real scale and reliability, exactly the customer profile the smaller, undercapitalized neoclouds struggle to attract. Nebius isn't a single product company either, which matters given how many names on that chart have no other legs to stand on if GPU rental margins compress. The company owns Avride, an autonomous driving and delivery robotics business with partnerships with Uber and Hyundai, TripleTen, a tech re skilling edtech platform and holds equity stakes in ClickHouse, the database company it spun out and recently backed in a funding round, and in Toloka, an AI data-labeling platform that sold a majority stake to Bezos Expeditions and Shopify in 2025. Those side businesses give Nebius optionality and diversified cash flow that a converted mining rig operator simply doesn't have. Bullish on Nebius, make sure to follow Melvin for more AI infrastructure insights, and if you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can check out the link below for more.

Melvin

37,428 views • 28 days ago

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
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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 views • 1 month ago

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Long Nebius and make sure to follow me Melvin for more underlooked AI oppurtunities.
0:20
MelvinInvests's profile picture

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Long Nebius and make sure to follow me Melvin for more underlooked AI oppurtunities.

Melvin

69,634 views • 2 months ago

Nebius will be the first trillion dollar neocloud hyperscaler. Most neoclouds are stuck in a single business model, renting bare GPU capacity to whoever will pay for it. Nebius is deliberately building across four layers instead, bare metal, managed infrastructure, inference, and eventually agentic tooling and each layer up the stack dramatically expands who can actually buy from them. Bare metal has maybe a dozen viable customers worldwide, since only the biggest players can even use raw infrastructure at that scale. Managed infrastructure opens that up to hundreds of buyers, while inference reaches thousands of potential customers. Agentic services are still early, but they could eventually serve tens of thousands of developers building on top of the platform. That's the real engine behind a trillion dollar outcome, since a single layer rental business caps out far lower than a company selling into an expanding pyramid of customers at every altitude. There's also a strategic decision buried in how Nebius handles its biggest clients. Serving giants like Meta and Microsoft is a double edged sword, since those companies bring their own full software stack and only need physical infrastructure underneath it, which leaves very thin margin for Nebius to capture on top. Roman was explicit that the company's long term strategy is to avoid over relying on any single hyperscaler and instead build a diversified customer portfolio spanning every layer of the stack, so no single client can dictate terms or growth. He also pushed back on the idea that this business is commodity, arguing that keeping up with what a Meta or Microsoft actually demands from infrastructure at true hyperscale is genuinely difficult, which is exactly why most emerging neoclouds can't even compete for that tier of client. The numbers from this week back up the strategy because revenue came in at 582 million dollars, up 454% year over year, while annualized recurring revenue hit 3.0 billion dollars, up 58% quarter over quarter. Four separate customer contracts signed during the quarter were each worth more than 1 billion dollars in total contract value. Pricing power tells the same story from a different angle. Nebius's newest capacity auction cleared 15% above any price it had ever charged before, and short notice hardware is now going for 40 to 50 million dollars per megawatt, roughly four to five times the 9.8 million dollar per megawatt baseline from earlier deals. That kind of pricing trajectory, paired with a push into higher margin inference and agentic layers, builds a revenue mix that scales well past what a pure infrastructure landlord could ever reach. There are a few other pieces that make Nebius structurally different from the rest of the pack because it owns its full vertical stack, from data center design to server racks to the software layer running on top of all of it. It also has early access to Nvidia's next-generation Vera Rubin platform, following Nvidia's 9.3% stake in the company, and it holds side businesses in autonomous driving through Avride and data infrastructure through ClickHouse and Toloka. Nebius is building far more than a GPU rental business, and I think the market is still underestimating how big that full-stack platform can become. Bullish on Nebius becoming the first trillion-dollar neocloud hyperscaler, make sure to follow Melvin for more AI infrastructure insights, and if you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can check out the link for more.
3:07
MelvinInvests's profile picture

Nebius will be the first trillion dollar neocloud hyperscaler. Most neoclouds are stuck in a single business model, renting bare GPU capacity to whoever will pay for it. Nebius is deliberately building across four layers instead, bare metal, managed infrastructure, inference, and eventually agentic tooling and each layer up the stack dramatically expands who can actually buy from them. Bare metal has maybe a dozen viable customers worldwide, since only the biggest players can even use raw infrastructure at that scale. Managed infrastructure opens that up to hundreds of buyers, while inference reaches thousands of potential customers. Agentic services are still early, but they could eventually serve tens of thousands of developers building on top of the platform. That's the real engine behind a trillion dollar outcome, since a single layer rental business caps out far lower than a company selling into an expanding pyramid of customers at every altitude. There's also a strategic decision buried in how Nebius handles its biggest clients. Serving giants like Meta and Microsoft is a double edged sword, since those companies bring their own full software stack and only need physical infrastructure underneath it, which leaves very thin margin for Nebius to capture on top. Roman was explicit that the company's long term strategy is to avoid over relying on any single hyperscaler and instead build a diversified customer portfolio spanning every layer of the stack, so no single client can dictate terms or growth. He also pushed back on the idea that this business is commodity, arguing that keeping up with what a Meta or Microsoft actually demands from infrastructure at true hyperscale is genuinely difficult, which is exactly why most emerging neoclouds can't even compete for that tier of client. The numbers from this week back up the strategy because revenue came in at 582 million dollars, up 454% year over year, while annualized recurring revenue hit 3.0 billion dollars, up 58% quarter over quarter. Four separate customer contracts signed during the quarter were each worth more than 1 billion dollars in total contract value. Pricing power tells the same story from a different angle. Nebius's newest capacity auction cleared 15% above any price it had ever charged before, and short notice hardware is now going for 40 to 50 million dollars per megawatt, roughly four to five times the 9.8 million dollar per megawatt baseline from earlier deals. That kind of pricing trajectory, paired with a push into higher margin inference and agentic layers, builds a revenue mix that scales well past what a pure infrastructure landlord could ever reach. There are a few other pieces that make Nebius structurally different from the rest of the pack because it owns its full vertical stack, from data center design to server racks to the software layer running on top of all of it. It also has early access to Nvidia's next-generation Vera Rubin platform, following Nvidia's 9.3% stake in the company, and it holds side businesses in autonomous driving through Avride and data infrastructure through ClickHouse and Toloka. Nebius is building far more than a GPU rental business, and I think the market is still underestimating how big that full-stack platform can become. Bullish on Nebius becoming the first trillion-dollar neocloud hyperscaler, make sure to follow Melvin for more AI infrastructure insights, and if you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can check out the link for more.

Melvin

28,932 views • 28 days ago

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
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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 views • 1 month ago

Morgan Stanley just dropped numbers that should make every investor pay attention (Save this). Hyperscalers spent $261B in 2024, they're now projected to spend $1.4T in 2028, a 5x increase in four years and that doesn't even include OpenAI or Anthropic. For the first time, Morgan Stanley is classifying SpaceX as a legitimate hyperscaler alongside Google, Amazon, Microsoft, and Meta. All of it flows into chips, data centers and memory and the supply chain companies sitting beneath the hyperscalers are the ones that will compound quietly. The most underrated play is memory. Micron is the only American HBM supplier, giving it a structural edge in government AI contracts that Samsung and SK Hynix cannot touch. Its entire 2026 HBM4 production is already sold out, revenue nearly tripled to $23.9B, and the memory prices are roughly doubling every year. The cooling problem is one of the most profitable bottlenecks in this entire trade. Vertiv makes the power management, liquid cooling systems and racks that keep GPU clusters from melting and it's up over 100% year to date in 2026 with a $15B+ backlog and guidance raised to $13.5–$14B in full year revenue. Arista Networks (ANET) is the networking infrastructure play, every AI data center needs ultra high speed networking fabric to connect thousands of GPUs together And Arista just doubled its 2026 AI revenue target as the industry shifts from proprietary InfiniBand to Open Ethernet, a shift that plays directly into Arista's strengths. Astera Labs solves the interconnect bottleneck inside data centers, the problem of getting data between chips fast enough to keep up with the GPUs. Revenue grew 93% year over year, it's already profitable, and its customers are Microsoft and Amazon directly. The hyperscalers are the miners and the real money is in the companies selling them the shovels, the electricity, the memory, the cooling, the networking, and the custom silicon. Make sure to follow me Melvin for more underrated infrastructure plays.
3:58
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Morgan Stanley just dropped numbers that should make every investor pay attention (Save this). Hyperscalers spent $261B in 2024, they're now projected to spend $1.4T in 2028, a 5x increase in four years and that doesn't even include OpenAI or Anthropic. For the first time, Morgan Stanley is classifying SpaceX as a legitimate hyperscaler alongside Google, Amazon, Microsoft, and Meta. All of it flows into chips, data centers and memory and the supply chain companies sitting beneath the hyperscalers are the ones that will compound quietly. The most underrated play is memory. Micron is the only American HBM supplier, giving it a structural edge in government AI contracts that Samsung and SK Hynix cannot touch. Its entire 2026 HBM4 production is already sold out, revenue nearly tripled to $23.9B, and the memory prices are roughly doubling every year. The cooling problem is one of the most profitable bottlenecks in this entire trade. Vertiv makes the power management, liquid cooling systems and racks that keep GPU clusters from melting and it's up over 100% year to date in 2026 with a $15B+ backlog and guidance raised to $13.5–$14B in full year revenue. Arista Networks (ANET) is the networking infrastructure play, every AI data center needs ultra high speed networking fabric to connect thousands of GPUs together And Arista just doubled its 2026 AI revenue target as the industry shifts from proprietary InfiniBand to Open Ethernet, a shift that plays directly into Arista's strengths. Astera Labs solves the interconnect bottleneck inside data centers, the problem of getting data between chips fast enough to keep up with the GPUs. Revenue grew 93% year over year, it's already profitable, and its customers are Microsoft and Amazon directly. The hyperscalers are the miners and the real money is in the companies selling them the shovels, the electricity, the memory, the cooling, the networking, and the custom silicon. Make sure to follow me Melvin for more underrated infrastructure plays.

Melvin

36,068 views • 1 month ago

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.
4:44
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This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

Melvin

34,306 views • 2 months ago

Jim Cramer just went on Mad Money and told investors to stay away from Nebius and that alone might be the strongest buy signal of the week (Save this). "Nebius is at the nexus of the craziness right now," Cramer said. "This stock is not done going down. There will be another time to buy it, but that time is not now." If you were waiting for one more confirmation, this week handed you several and the fundamentals moved the opposite direction from the stock price. Job postings in Singapore for Data Center Project Development, Site Selection & Colocation and Technical Due Diligence show Nebius actively scouting a new market, following similar signals in Wales and India within the same four day window. Job postings are a leading indicator that typically show up before a formal data center announcement, not after. Now here is the actual fundamental news from this week and it matters. Nebius announced a brand new business model, asset light infrastructure partnerships. Instead of financing every data center itself, outside partners will finance, own and operate the physical facilities. Nebius supplies the systems architecture, hardware design, software stack and its global sales organization. This is the same playbook major cloud providers and chipmakers have used for years, outsource the capital heavy physical layer, keep the high margin design and customer relationship layer. This directly addresses Nebius's biggest historical risk, the capital intensity of building GPU data centers fast enough to keep pace with demand. Nebius has already signed initial partnership deals under the new structure, meaning capacity can now scale globally without spending its own capital on land, steel, and power. None of this guarantees the stock stops falling in the short term, momentum is negative and the stock sits roughly 40% below its June all time high. The stock may keep falling but the thesis just got stronger and I am buying the company Nebius is becoming. Extremely bullish on Nebius and make sure to follow me Melvin for more underrated gems.
0:21
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Jim Cramer just went on Mad Money and told investors to stay away from Nebius and that alone might be the strongest buy signal of the week (Save this). "Nebius is at the nexus of the craziness right now," Cramer said. "This stock is not done going down. There will be another time to buy it, but that time is not now." If you were waiting for one more confirmation, this week handed you several and the fundamentals moved the opposite direction from the stock price. Job postings in Singapore for Data Center Project Development, Site Selection & Colocation and Technical Due Diligence show Nebius actively scouting a new market, following similar signals in Wales and India within the same four day window. Job postings are a leading indicator that typically show up before a formal data center announcement, not after. Now here is the actual fundamental news from this week and it matters. Nebius announced a brand new business model, asset light infrastructure partnerships. Instead of financing every data center itself, outside partners will finance, own and operate the physical facilities. Nebius supplies the systems architecture, hardware design, software stack and its global sales organization. This is the same playbook major cloud providers and chipmakers have used for years, outsource the capital heavy physical layer, keep the high margin design and customer relationship layer. This directly addresses Nebius's biggest historical risk, the capital intensity of building GPU data centers fast enough to keep pace with demand. Nebius has already signed initial partnership deals under the new structure, meaning capacity can now scale globally without spending its own capital on land, steel, and power. None of this guarantees the stock stops falling in the short term, momentum is negative and the stock sits roughly 40% below its June all time high. The stock may keep falling but the thesis just got stronger and I am buying the company Nebius is becoming. Extremely bullish on Nebius and make sure to follow me Melvin for more underrated gems.

Melvin

27,692 views • 1 month ago

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

Melvin

36,155 views • 2 months ago

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

Melvin

27,296 views • 2 months ago

This is WILD! Morgan Stanley projects a $7.5 trillion humanoid robot market by 2050 roughly one robot for every six people alive on Earth today (Save this). That number is so large it is almost impossible to process so here is the context, the entire humanoid robot industry generated literally zero commercial revenue in 2024. In 2025 it generated approximately $3 billion and that gap between where this industry is today and where it is the investment opportunity. Every transformational technology industry looks embryonic at exactly the moment before it inflects, the internet in 1995, smartphones in 2007, cloud in 2010. In every case, the investors who looked at the current revenue and said this is too small to matter missed the entire compounding run. The investors who looked at the structural driver and asked what happens to every industry when this technology becomes cheap and ubiquitous, captured generational returns. The structural driver here is the global labor market, which is worth approximately $30 trillion annually. Morgan Stanley estimates that roughly 75% of occupations and 40% of all employees in the United States have some degree of humanoidability meaning their work could, at some price point, be performed by a humanoid robot. That addressable market in the US alone is approximately $3 trillion and globally, the prize is the largest labor replacement opportunity in the history of capitalism. The cost curve is the key that unlocks it all. A humanoid robot cost roughly $200,000 in 2024, Morgan Stanley projects that falls to approximately $150,000 by 2028, and to $50,000 by 2050 in high-income countries and as low as $15,000 where Chinese supply chains dominate. At $50,000, the economics become compelling across an enormous range of manufacturing, logistics, warehousing and service jobs. At $15,000, they become compelling for consumer applications as well and the cost trajectory is essentially the same one the semiconductor industry has followed for 60 years and it produces the same outcome every time. The hardware component market alone is expected to reach $780 billion by 2040 as a result. The demand multipliers for specific components by 2050 compared to 2025 levels are staggering, edge compute chips up 1,904x, bearings up 370x, motors up 170x and precision reducers up 157x. Every one of those supply chain layers represents a distinct investment category that barely exists in meaningful scale today. Bullish on Robotics and make sure to follow me Melvin for more overlooked opportunities in robotics.
4:22
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This is WILD! Morgan Stanley projects a $7.5 trillion humanoid robot market by 2050 roughly one robot for every six people alive on Earth today (Save this). That number is so large it is almost impossible to process so here is the context, the entire humanoid robot industry generated literally zero commercial revenue in 2024. In 2025 it generated approximately $3 billion and that gap between where this industry is today and where it is the investment opportunity. Every transformational technology industry looks embryonic at exactly the moment before it inflects, the internet in 1995, smartphones in 2007, cloud in 2010. In every case, the investors who looked at the current revenue and said this is too small to matter missed the entire compounding run. The investors who looked at the structural driver and asked what happens to every industry when this technology becomes cheap and ubiquitous, captured generational returns. The structural driver here is the global labor market, which is worth approximately $30 trillion annually. Morgan Stanley estimates that roughly 75% of occupations and 40% of all employees in the United States have some degree of humanoidability meaning their work could, at some price point, be performed by a humanoid robot. That addressable market in the US alone is approximately $3 trillion and globally, the prize is the largest labor replacement opportunity in the history of capitalism. The cost curve is the key that unlocks it all. A humanoid robot cost roughly $200,000 in 2024, Morgan Stanley projects that falls to approximately $150,000 by 2028, and to $50,000 by 2050 in high-income countries and as low as $15,000 where Chinese supply chains dominate. At $50,000, the economics become compelling across an enormous range of manufacturing, logistics, warehousing and service jobs. At $15,000, they become compelling for consumer applications as well and the cost trajectory is essentially the same one the semiconductor industry has followed for 60 years and it produces the same outcome every time. The hardware component market alone is expected to reach $780 billion by 2040 as a result. The demand multipliers for specific components by 2050 compared to 2025 levels are staggering, edge compute chips up 1,904x, bearings up 370x, motors up 170x and precision reducers up 157x. Every one of those supply chain layers represents a distinct investment category that barely exists in meaningful scale today. Bullish on Robotics and make sure to follow me Melvin for more overlooked opportunities in robotics.

Melvin

15,545 views • 2 months ago

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