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.

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.

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.

Dan Ives says we're less than 15% through the AI buildout, with $3-4T of spending coming. "My five favorite picks for this year going in 2027, it's NVIDIA, it's Microsoft, it's Apple, it's Palantir, and it's CrowdStrike..." "Apple, which is really gonna be the toll collector on the consumer AI highway." He sees 800 to 1,000 data centers built over the next 2-3 years and calls memory "a true super cycle." "The bears have called 10 of the last two downturns." Is he right that we're in year 3 of a 10-12 year build, or is that already priced into $NVDA and $PLTR? FT @DivesTech @ZetaGlobal.

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.

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.

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

How could you POSSIBLY be bearish on compute right now when GPU rental prices are doing THIS? According to data from FastGPU, GPU rental prices increased as many as 81 times across providers over the past 60 days, with some of the most important chips used for AI training and inference seeing MASSIVE price increases. NVIDIA B300 rental prices increased 52.8%, going from $8.50 to $12.99 per GPU hour. H200 prices jumped 33.3%, B200 prices increased 27.8% and H100 prices rose 30% and the RTX PRO 6000? Rental prices surged an absolutely INSANE 307%. We're watching AI infrastructure companies get absolutely DESTROYED in the stock market, with investors freaking out about debt, financing costs, oversupply, and whether these companies will ever generate attractive returns on their investments. Meanwhile, the actual cost of renting the underlying compute is going UP. If we were genuinely heading toward a massive compute oversupply, you would expect rental prices to start collapsing as providers competed to fill their available capacity. Instead, we're seeing pricing strength across some of the most important GPUs powering the AI industry. And remember, we're still in the EARLY stages of AI adoption. Frontier labs are scaling their training clusters, inference demand is exploding as more people and businesses adopt AI, and the amount of compute required to serve increasingly sophisticated models continues to grow. This is EXACTLY why I've been so bullish on companies like $NBIS , $MU , $CRDO , $IREN etc. I genuinely believe compute will remain one of the most valuable resources in the AI economy for years to come. And while Wall Street continues panicking over the short term volatility, I'm BUYING THE DIP. If you want to know exactly what I’m buying on this dip and how I’m positioning across the compute buildout, check out portfolio below.

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.

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.

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.

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.

The AI compute shortage is getting WORSE and what's happening with NVIDIA's B200 GPUs right now is absolutely INSANE. According to Ornn Exchange, rental prices for NVIDIA's B200 GPUs just reached a new all time high of $8.54 per GPU hour, with prices climbing another 12.9% in just the past trading week. Normally, as technology gets older and more capacity comes online, you would expect rental prices to gradually decline. But we're seeing the exact opposite happening with B200s. Rental prices are moving higher, which shows that customers are willing to pay a premium just to secure access to compute. This is an incredibly BULLISH signal for the economics of AI infrastructure because higher utilization and stronger rental pricing can translate into better returns on deployed GPUs, especially for operators that have already secured capacity. And this is exactly why I remain so bullish on the long term opportunity for neoclouds. These companies are spending billions building out AI infrastructure and one of the biggest concerns investors have had is whether all this capacity will eventually lead to oversupply and collapsing rental prices but right now, the data is telling us a VERY different story. And demand for compute is already this strong TODAY, imagine what happens as these technologies begin scaling across the global economy.

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.