$MRVL is reportedly in advanced talks to acquire $LITE and this actually makes a lot of sense. Marvell is already a major player in custom AI chips and networking, while Lumentum makes the lasers and optical components needed to move data between GPUs at extremely high speeds. And as AI clusters get bigger, the connections between those chips are becoming just as important as the chips themselves. That's why we're seeing such a big shift toward optical networking. What's interesting is that Marvell already acquired Celestial AI to expand into optical connectivity. Buying Lumentum would take that a step further by giving Marvell access to its laser technology, manufacturing capacity, and customer relationships. And remember, Lumentum has already talked about certain optical products being sold out through early 2029, with customers discussing demand into 2030. Marvell could be trying to control a much larger part of the AI networking supply chain, especially as the industry moves away from copper and toward optical connections.

This might be the most underrated AI story of today. According to CNBC, TikTok's US business is looking to secure multiple gigawatts of computing capacity, with one source suggesting the company is targeting 4 GW by the early 2030s. I don't think people understand how significant this is. We're talking about a social media platform potentially looking to secure enough power and computing infrastructure to rival some of the largest AI data center developments in the country. For perspective, Meta's massive Hyperion data center campus in Louisiana is targeting around 5 GW of capacity. TikTok already relies primarily on Oracle Cloud Infrastructure, and the company has confirmed that $ORCL will remain its primary cloud provider in the United States. On top of that, TikTok is retraining its recommendation algorithm using American user data and is developing AI products focused on large model inference and creative automation. In other words, this isn't necessarily just about supporting people scrolling through videos anymore. TikTok could be preparing for significantly more AI workloads which would require enormous amounts of computing power. Now connect this to everything else we've been discussing. Microsoft, Amazon, Google, Meta, OpenAI, and xAI are already competing to secure power, GPUs, land, and data center capacity. Now we're seeing another major technology platform potentially entering that same competition at the gigawatt scale. And remember, these companies cannot simply decide they want another gigawatt of computing capacity and have it available tomorrow. They need electricity, substations, cooling infrastructure, networking equipment, GPUs, memory, and facilities capable of supporting all of it. This is exactly why I remain so bullish on the companies supplying the infrastructure behind AI. Whether we're talking about $NVDA, $MU, $ORCL, $BE, or the broader networking and power ecosystem, the number of potential customers competing for limited resources continues to grow. What's particularly interesting is that this demand is increasingly coming from companies outside the handful of frontier AI labs everyone focuses on. I think we're still underestimating just how much infrastructure the AI economy is going to need over the next decade.

This selloff is a lot closer to the bottom than we think. According to Jefferies, over the past 10 trading days, its clients have been net sellers of more than $1B worth of technology stocks on 7 separate days. This chart tracks the buying and selling activity of Jefferies’ clients over a rolling 20 day period. When the purple line is above zero, investors are buying more than they’re selling. When it’s below zero, they’re selling more than they’re buying. Right now, that number has collapsed to approximately negative $11B, making this one of the most extreme periods of technology selling we’ve seen in the past two years. For comparison, even during the major selloffs earlier in 2025 and 2026, the selling pressure wasn’t consistently this severe. And remember, this is just one investment bank’s trading activity. This also helps explain why companies across semiconductors, memory, power, and AI infrastructure have been getting absolutely crushed despite reporting strong earnings and continued demand. A lot of this selling isn’t necessarily happening because the fundamentals have changed. Large institutions are reducing exposure, managing risk and raising cash. When everyone starts selling at the same time, even the best companies get dragged down. Now, does this mean the bottom is definitely here? Of course not. Nobody knows that but historically, when selling becomes this extreme, it can be a sign that we’re getting closer to exhaustion. Eventually, the forced selling slows down, buyers return, and stocks can recover. My personal view is that we’re likely getting much closer to a bottom, I’m not changing my long term outlook on AI because hedge funds and institutions are aggressively selling technology stocks. The demand for compute, memory, power, and AI infrastructure hasn’t suddenly disappeared. I’m investing with years in mind, not weeks.

If you're wondering why the market keeps selling off, take a look at these two charts from Goldman Sachs. The chart on the left shows how much hedge funds have been buying versus selling throughout 2026. When the black line moves higher, it means hedge funds are buying more than they're selling. When it moves lower, it means they're selling more than they're buying. Notice those three red boxes in April, August, and October? Each one highlights a period of extremely aggressive selling, with the latest reading approaching the lows we saw earlier this year. In other words, hedge funds have been dumping stocks at a pace similar to some of the worst selling periods we've experienced earlier this year. Now look at the chart on the right. The blue line represents how much leverage hedge funds are using on their long positions, meaning stocks they're betting will go higher. The red line represents leverage on their short positions, meaning stocks they're betting will go lower. Notice how the red line has climbed dramatically. According to Goldman Sachs, short leverage has now reached the 99th percentile over the past five years. In simpler terms, hedge funds are carrying some of their largest bearish positions in years. And this helps explain why we're seeing so much pressure across AI, semiconductors, memory, and other high growth stocks. Not only are institutions selling their existing positions but they're also increasing their bets that stocks will continue falling. And as someone said unfortunately, we're just collateral damage in the battles between hedge funds. What's even crazier is that some of these hedge funds have been getting absolutely hammered themselves and some funds are under tremendous pressure to recover their losses. And who knows, maybe aggressively shorting the market is their grand plan to finally come back to life before the year ends, haha. I'm obviously being a little sarcastic, but when large institutions start selling billions of dollars worth of stocks, retail investors get caught in the middle. Even companies reporting strong earnings and growing their businesses can get dragged down simply because large funds are reducing risk or betting against the broader market. But here's something important to understand.The same positioning that's putting pressure on stocks today could help fuel a pretty aggressive recovery. When investors short a stock, they eventually have to buy it back to close that position. If the market starts recovering, some hedge funds may rush to cover their shorts, creating additional buying pressure. That's why markets can sometimes recover just as aggressively as they sell off. Of course, none of this means we've reached the bottom. Hedge funds can continue selling, and bearish positioning can remain elevated for quite some time. But I think it's important to recognize that a lot of this selling is being amplified by institutional positioning. It doesn't necessarily mean the long term fundamentals of every company being sold have suddenly deteriorated. My long term conviction in AI infrastructure hasn't changed, regardless of how ugly this gets.

Just a friendly reminder that the Federal Reserve is meeting next week and markets are now pricing in a 90% probability that interest rates remain unchanged. This is pretty important, especially considering everything we've been seeing across the broader market over the past few weeks. Inflation is still running hotter than expected, Treasury yields remain elevated but have started to come down a bit and investors are increasingly concerned that interest rates could stay higher for longer. The bigger question isn't necessarily whether the Fed holds rates steady next week, because that's already widely expected but rather what Fed Chair Kevin Warsh signals about the months ahead. Remember, the Fed raised rates in September, and now we're seeing expectations shift toward a potential pause in October, with economists increasingly looking toward another hike in December. For equities, especially high growth technology stocks, this matters quite a bit. Higher interest rates increase borrowing costs, put pressure on valuations, and make financing more expensive for capital-intensive businesses like neoclouds. And with hundreds of billions of dollars being poured into AI infrastructure, the cost of capital is becoming increasingly important. Personally, I'm going to be paying much closer attention to Warsh's commentary and the bond market than the actual rate decision. A pause might provide some temporary relief but if the Fed signals that additional hikes are coming, we could see volatility continue.

I know the market has been absolutely BRUTAL lately, especially for those of us invested heavily in AI, semiconductors, and other high growth stocks. But I think it's important to put what's happening into perspective. Right now, investors are dealing with inflation, elevated bond yields, Iran war, Midterms, concerns about AI spending, and uncertainty around how some of these massive infrastructure investments will be financed. In 2020, the S&P 500 collapsed roughly 34% during COVID, only to recover its losses within months. In 2022, the Nasdaq fell more than 30% as inflation surged and the Federal Reserve aggressively raised interest rates. Many investors genuinely believed the technology trade was finished. Yet the market eventually recovered and went on to make new highs. Now, I'm not saying every beaten down stock will recover, because plenty won't. And I'm certainly not saying we've reached the bottom. But history has repeatedly shown us that some of the greatest long term investment opportunities have appeared during periods when investor sentiment was at its worst. And that's exactly why I bought the dip. I genuinely believe we're living through one of the greatest technological advancements in human history. AI is going to fundamentally change how we work, how businesses operate and how the global economy functions. But that doesn't mean the journey is going to be a straight line higher. There are going to be plenty of bumps along the way. There will be corrections, periods of uncertainty, companies that fail and moments when investors begin questioning whether the entire opportunity was overhyped. We've seen this happen during nearly every major technological revolution, and I don't expect AI to be any different. My long term conviction in AI infrastructure hasn't changed. The demand for compute, memory, networking, and power isn't suddenly disappearing because stocks are selling off. And building wealth in the stock market is NOT easy, if it were, everyone would be rich. Everyone wants the massive returns, but very few people are willing to endure the volatility, uncertainty, and patience required to get there. Of course, conviction alone isn't enough. You still need to manage risk, understand what you own, and recognize when the fundamentals actually change. But I'm not going to let a few difficult weeks in the market distract me from an investment opportunity I believe will play out over the next decade. Think in decades, not days.

Nebius just secured a $3.5 billion financing facility from Citi to help fund its global AI infrastructure expansion, including data centers and GPU purchases. What makes this particularly interesting is the financing terms. The facility has a 364 day maturity and carries an initial interest rate of SOFR + 2.75%, potentially increasing to SOFR + 3.50%. SOFR is essentially a benchmark interest rate that banks use when lending money, meaning Nebius is borrowing at a relatively modest premium above that benchmark. Now, remember one of the biggest concerns surrounding neoclouds recently has been their ability to secure financing at attractive rates. Investors have been worried that companies like Nebius and CoreWeave would struggle to raise the billions of dollars needed to continue expanding, especially with borrowing costs remaining elevated and credit markets becoming increasingly cautious. Well, this announcement directly challenges that concern, at least for Nebius. Not only has Nebius secured another $3.5 billion in financing, but it has managed to do so at what I consider fairly attractive rates for a capital intensive AI infrastructure company. And here’s another important point. Nebius already has more than $40 billion in long term customer commitments from investment grade customers, including Microsoft and Meta. Those contracts give lenders greater confidence that the infrastructure being financed will eventually generate revenue. I’ve said this repeatedly, I don’t believe every neocloud will survive this AI infrastructure buildout. Access to capital is going to separate the winners from the losers, and Nebius continues to demonstrate why I believe it will be among the survivors. This is a very encouraging development for $NBIS shareholders, especially after all the concerns surrounding financing and the recent selloff.

OpenAI is starting to get NERVOUS about Anthropic's upcoming IPO. OpenAI is reportedly looking to raise another $30B at a staggering $1.4T valuation, and I don't think the timing is a coincidence. Anthropic has reportedly been targeting a valuation as high as $2T, and if it manages to go public anywhere near those levels, we're talking about one of the largest IPOs in history. That could attract an ENORMOUS amount of institutional capital, especially from investors looking for direct exposure to the AI model race. There is only so much liquidity available in the market at any given time. If Anthropic gets to the public markets first and starts absorbing tens of billions of dollars in investor capital, OpenAI could find itself competing for that same pool of money, potentially at a much less favorable valuation. And here's another huge risk. If Anthropic's IPO ends up flopping, whether that's weak investor demand, disappointing pricing or the stock collapsing after going public, it could have serious implications for OpenAI as well. Investors could start questioning the valuations being assigned to these AI companies, making it significantly harder for OpenAI to justify a $1.4T valuation or raise capital on favorable terms. This is why I think OpenAI has EVERY incentive to secure as much capital as possible before Anthropic makes its move. Not only does it strengthen OpenAI's balance sheet but it also gives the company more financial flexibility to continue investing in compute, infrastructure, talent, and model development while securing a massive valuation benchmark ahead of a potential IPO. And remember, these companies are competing for more than just customers. They're competing for GPUs, data centers, electricity, engineering talent, and increasingly, investor capital. I wouldn't be surprised if we see both companies become much more aggressive with fundraising over the coming months. This is turning into an absolute capital arms race, and whoever can secure the most funding on the best terms could have a major advantage as the AI industry continues scaling.

According to Korean media, Elon Musk's Terafab is now talking to almost EVERY Korean chip supplier it can find. Until now, the only known Korean suppliers were HPSP and Hanmi Semiconductor. Now TheElec reports that Tesla and SpaceX are in talks across nearly every category Korean companies can supply, from basic piping parts to process materials to test components. At SEMICON West in San Francisco last week, Terafab staff walked straight to around 10 Korean booths on the morning of day one, including ENF Technology, Dongjin Semichem, Hanmi Semiconductor, Unisem and MiCo. Several companies have already signed NDAs. One got an NDA by email the same evening Terafab visited its booth. Hanmi Semiconductor is discussing TC bonders for Terafab, and HPSP has agreed to supply one high pressure hydrogen annealing tool to the R&D line. MiCo's cleaning unit Komico signed a three way NDA with Tesla and SpaceX about a month ago, and ISC is already swapping samples for rubber test sockets. Tesla and SpaceX do not buy from Chinese suppliers. One Korean supplier put it simply, without China there are only a few options, and Japan is expensive. They also want prices that are CHEAPER than China. $LRCX and $AMAT have reportedly built dedicated Terafab teams, and Fujifilm is meeting Terafab buyers regularly on slurries, photoresist and etchants. The goal is a fab with 1 million wafer starts per month within 10 years, with $16.8 billion of initial investment and more than 3,000 jobs. SpaceX broke ground in Grimes County, Texas this month, and the first 100,000 wafer per month phase is expected before 2030. Logic is being done with $INTC, talks with $TSM are ongoing, and memory will be built separately. One supplier said that if they email Terafab in the middle of the night in the United States, they get an answer in 30 minutes to an hour. $TSLA and $SPCX are building a chip supply chain at Elon speed.

The HBM market is setting up for one of the BIGGEST supply demand imbalances in the entire AI buildout. Just look at what Bloomberg Intelligence is projecting. HBM shipments could grow from roughly 2.5 billion GB in 2025 to nearly 18 billion GB by 2030. That's approximately a 7X increase in volume in just FIVE years. Normally, when an industry dramatically increases production, you would expect prices to decline as supply catches up with demand. Yet Bloomberg's projections are showing the complete opposite. Average HBM prices are expected to climb from roughly $13 per GB in 2025 to $35 per GB by 2030. Think about how insane that is. We're potentially looking at a market where shipments increase roughly 7X while average selling prices nearly triple. And this is exactly why I'm so bullish on memory. Every new generation of AI accelerators requires increasingly sophisticated memory to deliver the bandwidth needed to keep those GPUs running efficiently. And as models become larger, inference workloads explode, and autonomous agents and physical AI begin scaling, the demand for memory is only going to accelerate. What makes HBM particularly interesting is that supply cannot simply expand overnight. Manufacturing HBM requires advanced packaging, complex stacking processes, enormous capital investment, and significant production capacity. Not to mention, expanding HBM production can consume manufacturing capacity that would otherwise go toward conventional DRAM, potentially tightening supply across the broader memory market as well. This is why I continue to believe memory is one of the most attractive opportunities in the AI infrastructure trade.

The HBM market is setting up for one of the BIGGEST supply demand imbalances in the entire AI buildout. Just look at what Bloomberg Intelligence is projecting. HBM shipments could grow from roughly 2.5 billion GB in 2025 to nearly 18 billion GB by 2030. That's approximately a 7X increase in volume in just FIVE years. Normally, when an industry dramatically increases production, you would expect prices to decline as supply catches up with demand. Yet Bloomberg's projections are showing the complete opposite. Average HBM prices are expected to climb from roughly $13 per GB in 2025 to $35 per GB by 2030. Think about how insane that is. We're potentially looking at a market where shipments increase roughly 7X while average selling prices nearly triple. And this is exactly why I'm so bullish on memory. Every new generation of AI accelerators requires increasingly sophisticated memory to deliver the bandwidth needed to keep those GPUs running efficiently. And as models become larger, inference workloads explode, and autonomous agents and physical AI begin scaling, the demand for memory is only going to accelerate. What makes HBM particularly interesting is that supply cannot simply expand overnight. Manufacturing HBM requires advanced packaging, complex stacking processes, enormous capital investment, and significant production capacity. Not to mention, expanding HBM production can consume manufacturing capacity that would otherwise go toward conventional DRAM, potentially tightening supply across the broader memory market as well. This is why I continue to believe memory is one of the most attractive opportunities in the AI infrastructure trade.

Power is the BIGGEST constraint in the entire AI buildout right now. Morgan Stanley estimates that US data centers could require roughly 160 GW of power by 2029, but even after accounting for data centers under construction, additional utility grid access, behind the meter generation, and potential nuclear capacity, there could STILL be a 76.5 GW power shortfall. Think about how INSANE that is. We're talking about nearly HALF of projected US data center power demand potentially going unmet and here's why this matters so much. We can manufacture more GPUs, build better AI models, and raise hundreds of billions of dollars to finance new infrastructure. But none of that matters if you can't actually power the data centers. Electricity isn't something you can simply scale overnight. Building new transmission infrastructure, expanding grid capacity, securing interconnection agreements, and developing new power generation can take YEARS. And with AI workloads becoming increasingly compute intensive, demand for electricity is only going to accelerate as inference, autonomous agents, robotics, and physical AI continue scaling. This is exactly why I think POWER is becoming one of the most valuable assets in the entire AI ecosystem. And I think @elonmusk understands this better than almost anyone and it's also why I remain so bullish on neoclouds like $NBIS, $IREN, $SPCX. These companies are becoming incredibly important because they're helping solve one of the biggest bottlenecks in the AI industry turning massive demand for compute into actual operational capacity. The companies that can secure large amounts of power, develop data center campuses, deploy GPUs, and get that infrastructure operational FAST are going to have an enormous competitive advantage. And as power becomes increasingly scarce, the value of existing infrastructure with secured electricity and grid access could become significantly more valuable. You can't have an AI revolution without compute, and you can't have compute without POWER. If you want to see how I’m investing around the AI power bottleneck and the infrastructure companies I think benefit most, check out my portfolio below.

Power is the BIGGEST constraint in the entire AI buildout right now. Morgan Stanley estimates that US data centers could require roughly 160 GW of power by 2029, but even after accounting for data centers under construction, additional utility grid access, behind the meter generation, and potential nuclear capacity, there could STILL be a 76.5 GW power shortfall. Think about how INSANE that is. We're talking about nearly HALF of projected US data center power demand potentially going unmet and here's why this matters so much. We can manufacture more GPUs, build better AI models, and raise hundreds of billions of dollars to finance new infrastructure. But none of that matters if you can't actually power the data centers. Electricity isn't something you can simply scale overnight. Building new transmission infrastructure, expanding grid capacity, securing interconnection agreements, and developing new power generation can take YEARS. And with AI workloads becoming increasingly compute intensive, demand for electricity is only going to accelerate as inference, autonomous agents, robotics, and physical AI continue scaling. This is exactly why I think POWER is becoming one of the most valuable assets in the entire AI ecosystem. And I think @elonmusk understands this better than almost anyone and it's also why I remain so bullish on neoclouds like $NBIS, $IREN, $SPCX. These companies are becoming incredibly important because they're helping solve one of the biggest bottlenecks in the AI industry turning massive demand for compute into actual operational capacity. The companies that can secure large amounts of power, develop data center campuses, deploy GPUs, and get that infrastructure operational FAST are going to have an enormous competitive advantage. And as power becomes increasingly scarce, the value of existing infrastructure with secured electricity and grid access could become significantly more valuable. You can't have an AI revolution without compute, and you can't have compute without POWER. If you want to see how I’m investing around the AI power bottleneck and the infrastructure companies I think benefit most, check out my portfolio below.

Market is making a MASSIVE mistake selling $NBIS right now. According to their estimates, Nebius is expected to go from just $1.25 billion in annualized revenue run rate in 2025 to nearly $8 billion in 2026, $16.8 billion in 2027, $30 billion in 2028, and an absolutely INSANE $71.7 billion by 2030. We're talking about a company that could potentially grow its annualized revenue run rate by more than 57 TIMES in just FIVE years. And even after reaching nearly $30 billion in 2028, William Blair still expects the business to grow another 69.6% in 2029 and 41% in 2030. Now, I know what everyone is thinking. What about the recent selloff? What about the debt? What about the enormous capital expenditures required to build all this infrastructure? These are legitimate concerns, and I'm not going to pretend otherwise. The market is clearly becoming increasingly concerned about how neocloud companies will finance their expansion, particularly as investors begin questioning whether the returns on all this spending will justify the investment. But here's where I think investors are making a HUGE mistake. They're treating every neocloud company as if they're operating the exact same business with the exact same economics. They're NOT. Nebius is building a full stack AI cloud platform, and what makes the company particularly interesting is how it's expanding across multiple layers of the AI ecosystem. They own and operate massive GPU clusters, data centers, networking infrastructure and the cloud software needed to train and run AI models at scale. But beyond that, they've also acquired Tavily, which provides real time web search capabilities for AI agents, and developed Nebius Token Factory, an inference platform that allows developers and enterprises to deploy AI models at scale. They've also strengthened their inference capabilities through Eigen AI and Clarifai's technology. And that's just the beginning. Nebius Group also owns Avride, which is developing autonomous vehicles and delivery robots in partnership with Uber, and TripleTen, an education technology business. The company also holds significant stakes in ClickHouse, one of the leading real time analytics database companies, and Toloka, which provides data and evaluation services for AI development. Now, not all of these businesses directly contribute to Nebius's core AI cloud revenue, but what excites me is the amount of OPTIONALITY the company has across the entire AI ecosystem. We're talking about exposure to AI infrastructure, model training, inference, AI agents, data analytics, and autonomous systems, all while the company continues securing major contracts with customers like Microsoft and Meta and expanding its global infrastructure footprint. And what's particularly interesting to me is HOW the company is financing this growth. Nebius has already demonstrated that it can secure financing backed by deployed GPUs and contracted customer cash flows, including its $775 MILLION secured debt facility. That's an important distinction because there is a MASSIVE difference between borrowing billions to speculate on future demand and financing infrastructure supported by contracted demand. Now, does that eliminate the risks? Absolutely NOT. Nebius still needs to execute, deploy enormous amounts of capital, manage its debt, and ensure that the economics of providing compute remain attractive as the industry scales. But I think investors are focusing so heavily on the risks that they're completely overlooking the potential scale of the business. And here's my biggest question. Has the long term demand for AI compute suddenly disappeared? Have enterprises stopped adopting AI? Have frontier labs stopped needing more infrastructure? Have hyperscalers suddenly decided they don't need additional capacity? NO, If anything, the underlying demand for AI compute continues to grow as models become more capable, inference workloads expand, and AI adoption accelerates across the economy. The industry still needs enormous amounts of infrastructure, and companies capable of delivering that capacity efficiently are positioned to capture a meaningful portion of that spending. What HAS changed is market sentiment. Investors have become increasingly concerned about AI infrastructure debt, capital expenditures, and whether the current pace of investment is sustainable. And those concerns have triggered a brutal selloff across the sector, including Nebius and CoreWeave. But from my perspective, the fundamental reason I invested in Nebius hasn't changed. The company is still pursuing an enormous opportunity, demand for AI compute remains strong, and its long term growth potential is still very much intact.

Market is making a MASSIVE mistake selling $NBIS right now. According to their estimates, Nebius is expected to go from just $1.25 billion in annualized revenue run rate in 2025 to nearly $8 billion in 2026, $16.8 billion in 2027, $30 billion in 2028, and an absolutely INSANE $71.7 billion by 2030. We're talking about a company that could potentially grow its annualized revenue run rate by more than 57 TIMES in just FIVE years. And even after reaching nearly $30 billion in 2028, William Blair still expects the business to grow another 69.6% in 2029 and 41% in 2030. Now, I know what everyone is thinking. What about the recent selloff? What about the debt? What about the enormous capital expenditures required to build all this infrastructure? These are legitimate concerns, and I'm not going to pretend otherwise. The market is clearly becoming increasingly concerned about how neocloud companies will finance their expansion, particularly as investors begin questioning whether the returns on all this spending will justify the investment. But here's where I think investors are making a HUGE mistake. They're treating every neocloud company as if they're operating the exact same business with the exact same economics. They're NOT. Nebius is building a full stack AI cloud platform, and what makes the company particularly interesting is how it's expanding across multiple layers of the AI ecosystem. They own and operate massive GPU clusters, data centers, networking infrastructure and the cloud software needed to train and run AI models at scale. But beyond that, they've also acquired Tavily, which provides real time web search capabilities for AI agents, and developed Nebius Token Factory, an inference platform that allows developers and enterprises to deploy AI models at scale. They've also strengthened their inference capabilities through Eigen AI and Clarifai's technology. And that's just the beginning. Nebius Group also owns Avride, which is developing autonomous vehicles and delivery robots in partnership with Uber, and TripleTen, an education technology business. The company also holds significant stakes in ClickHouse, one of the leading real time analytics database companies, and Toloka, which provides data and evaluation services for AI development. Now, not all of these businesses directly contribute to Nebius's core AI cloud revenue, but what excites me is the amount of OPTIONALITY the company has across the entire AI ecosystem. We're talking about exposure to AI infrastructure, model training, inference, AI agents, data analytics, and autonomous systems, all while the company continues securing major contracts with customers like Microsoft and Meta and expanding its global infrastructure footprint. And what's particularly interesting to me is HOW the company is financing this growth. Nebius has already demonstrated that it can secure financing backed by deployed GPUs and contracted customer cash flows, including its $775 MILLION secured debt facility. That's an important distinction because there is a MASSIVE difference between borrowing billions to speculate on future demand and financing infrastructure supported by contracted demand. Now, does that eliminate the risks? Absolutely NOT. Nebius still needs to execute, deploy enormous amounts of capital, manage its debt, and ensure that the economics of providing compute remain attractive as the industry scales. But I think investors are focusing so heavily on the risks that they're completely overlooking the potential scale of the business. And here's my biggest question. Has the long term demand for AI compute suddenly disappeared? Have enterprises stopped adopting AI? Have frontier labs stopped needing more infrastructure? Have hyperscalers suddenly decided they don't need additional capacity? NO, If anything, the underlying demand for AI compute continues to grow as models become more capable, inference workloads expand, and AI adoption accelerates across the economy. The industry still needs enormous amounts of infrastructure, and companies capable of delivering that capacity efficiently are positioned to capture a meaningful portion of that spending. What HAS changed is market sentiment. Investors have become increasingly concerned about AI infrastructure debt, capital expenditures, and whether the current pace of investment is sustainable. And those concerns have triggered a brutal selloff across the sector, including Nebius and CoreWeave. But from my perspective, the fundamental reason I invested in Nebius hasn't changed. The company is still pursuing an enormous opportunity, demand for AI compute remains strong, and its long term growth potential is still very much intact.

UBS says the 19% selloff in memory stocks is an opportunity, not a sign the cycle is breaking. AI agents are driving stronger server DDR5 orders into 2027. UBS expects HBM4 to grow from 17% of HBM demand in 2026 to 39% in 2027. It also expects Samsung to add 120,000 wafers a month of DRAM capacity by late 2027, but sees little real impact on supply until late 2028. $MU

China wants to control exports of its most powerful AI models. A new draft adds 23 AI technologies to its export control list, including large models, AI agents and computing clusters. Frontier models above 2 trillion parameters, or trained with more than 10^26 FLOPs, would need a license before their weights or source code leave the country. Open source models that are already public are exempt.

UBS says the 19% selloff in memory stocks is an opportunity, not a sign the cycle is breaking. AI agents are driving stronger server DDR5 orders into 2027. UBS expects HBM4 to grow from 17% of HBM demand in 2026 to 39% in 2027. It also expects Samsung to add 120,000 wafers a month of DRAM capacity by late 2027, but sees little real impact on supply until late 2028. $MU

According to Korean media, Samsung is considering an acquisition to build up its silicon photonics business. Samsung Foundry plans to enter the photonic chip market next year on 12 inch wafers. Target customers include optical module makers like $COHR and $LITE. The same report says $TSM is still about 1 to 2 years ahead with its COUPE platform. Optical is turning into one of the biggest bottlenecks in AI data centers, and Samsung does not want to miss it.

China just put its own chip know how on its export control list. The draft covers AI processor designs at 14nm or below, DRAM designs at 18nm or below, and advanced 3D NAND designs. It also covers the interconnect for AI clusters with more than 10,000 accelerators. This is still a proposal and public comments run until mid November. China is no longer just on the receiving end of chip restrictions.