Weekly Update: Macro + Port Top 7 | July 20-26

Weekly Update: Macro + Port Top 7 | July 20-26 Retail just had its worst month since 2022 while the S&P sat near highs. Why that gap is positioning, not a broken thesis, and where my top 7 sits going into a heavy earnings week. I’m making this a quick weekly piece: my top 7 holdings, where I actually make most of my money, short and to the point. Once a month, you get the full portfolio review with entry prices, my cost average, and my year-to-date returns. Why share this weekly? It’s not to show you how I’m doing. It’s to help you understand where my conviction lies, not in the particular names but in which layers. Why does that matter right now? Because the tape has been living history. These past weeks have been one of the harshest high beta selloffs on record. I won’t go into every reason; there are a lot of factors in play and I like to keep things simple. If one sector runs too hot for too long, profit-taking is coming from somewhere. What this has meant for retail A lot of retail has capitulated. Margin is only useful if you know how to use it properly, and if you can avoid it, avoid it. Many of you have stopped being net buyers and have become net sellers. So does that mean your intuition is wrong? Human nature says that when there is danger, you run. And there has been danger. Look at what the tape has done this month. You are right to feel hurt. For a lot of us money is the key to goals we want to reach as soon as possible. I’m working toward a number that means my family is taken care of regardless of anything else I do. Watching that drop this month has been painful. So I write this as much for myself as for all of you. I sincerely only care that we succeed, if not for ourselves then for the people we cherish most. If it makes you feel any better, the average retail portfolio looks horrid in 2026. It doesn’t make me feel better. If anything, it makes me want to push my work to a broader audience. Bloomberg’s basket of the 50 most popular retail stocks fell 13% in July alone, the steepest monthly decline for that group since 2022. A separate Jefferies tracker of Russell 1000 names with high retail ownership is down more than 25% since June. Meanwhile, SOXX, the closest thing to an AI buildout ETF, is still up roughly 75% year-to-date after peaking near +117% on June 22. The index is fine; the people trading it are not. So is the top in? Short answer: not even close. Long answer: hyperscaler CapEx projections keep climbing. Morgan Stanley has the four majors going from $413B in 2025 to $743B in 2026, $1,159B in 2027, and $1,287B in 2028. If anything I think those are conservative, because they price in AI and agentic AI as the pillars of growth and little else. That money comes from somewhere and it flows somewhere. It moves from the hyperscalers building the AI to the hardware it gets built on. Some of that spend is cyclical. Some of it, the way NVIDIA’s did, turns structural. We’re seeing the first signs of that in memory with long term agreements, but we need more complexity in the stack before that holds as a rule. Look at Alphabet. This is the first quarter of negative free cash flow in the company’s history. That is not a profitability problem, Google is still enormously profitable. It is capex outrunning operating cash flow, which is exactly what it looks like when a company decides the buildout matters more than the optics of a clean cash flow statement. If that doesn’t tell you where things are headed, I don’t know what will. And this doesn’t stop in 2028 or in 2030. AI compute, in particular inference, is becoming the new oil. Maybe not in 5 years, but in 25. AI itself is a stepping stone to whatever comes next, and all of it needs the same thing to operate: compute. The complexity that is hard to price in We are living through several things at once. On top of everything above, this is a midterm election year, which usually behaves differently from a normal year for the market. Read that again, because nothing about this year has been business as usual. And there are external factors adding uncertainty on top. I won’t do political commentary, but the current macro backdrop is its own level of complexity. Read more

Meta post about the author’s first month on Substack and a viral “AI buildout has twelve floors” map (app-to-gallium supply chain). No explicit tickers/cashtags, no valuation, positioning, catalyst timing, or tradeable callouts. Mostly context about AI buildout as an investing framework rather than actionable security-level evidence.

Post frames Nebius as a “NeoCloud”/GPU-specialized AI cloud infrastructure provider (“compute landlord”) with multi-year contracted demand, very rapid recent revenue/ARR growth, and an implied capacity-constrained buildout (“cannot build fast enough”). It positions Nebius within Layer 4 cloud infrastructure versus hyperscalers (AWS/Azure/GCP) and suggests demand visibility into early 2030s. The content is promotional/deep-dive style but contains several concrete business metrics that can support an investable view on Nebius; fewer explicit, tradable implications are made for other public tickers.

Post argues the best risk/reward in the “humanoid robot trade” is not humanoid OEM logos (e.g., Tesla, SPAC robot announcements) but repeat, scarce component suppliers—specifically joint actuators/gearboxes—using the author’s prior “one layer down” framework (cites SanDisk example from prior AI trade period). No explicit public component-supplier tickers are provided in the excerpt; most named entities are either OEMs or private companies.

Post argues for a long-term humanoid-robotics investment theme driven by demographics and wage pressure, but warns that “humanoid ETF” labels mask very different exposures (pure-play vs supply-chain vs legacy robotics rebranded). It emphasizes timeline risk: revenues are near-zero today and meaningful market size is mid-2030s+, with 2050 TAM figures often used misleadingly. No specific ETF tickers/names are provided in the excerpt, so there are no directly tradable ticker ideas supported by the text as given.

Post argues AI datacenter buildout is constrained/leveraged to Layer-6 memory/storage (NAND flash), claiming “SanDisk” (formerly inside Western Digital) is uniquely positioned with hyperscaler-scale NAND supply and new multi-year customer contracts, implying durable pricing/power and early-cycle upside. Mentions NVIDIA only as headline Layer-5 GPU beneficiary; emphasizes storage as the underappreciated bottleneck/necessity.

Post frames Agility Robotics as the only U.S. pure-play humanoid robotics company with paying customers going public via SPAC Churchill Capital Corp XI (CCXI). Deal announced Jun 24, 2026: $2.5B merger valuing Agility, >$620M cash to company (trust + Foxconn-led PIPE). CCXI up ~18% on announcement; expected ticker change to AGLT at close targeted for Q4 2026. Business model emphasized as “robotic labor subscription” (robot owned by Agility; rented monthly incl. software/maintenance), with key underwriting question: can ~100 deployed robots scale into a platform before competition and cash burn become limiting.

Analysis pending. The source event was captured, but automated analysis failed: LLM is required for source analysis but is unavailable

Post argues Micron (MU) is a critical bottleneck beneficiary of AI buildout because DRAM and especially HBM are scarce inputs required to keep GPUs/accelerators fed with data. It frames MU as having surpassed/beat guidance materially on revenue and EPS and highlights strategic positioning as the only U.S.-based memory manufacturer. Much of the price/market-cap commentary appears exaggerated/unverifiable, but the core investable implication is bullish MU via AI-driven memory demand (HBM/DRAM).