Wednesday, November 4, 2026

@Mr_Neutral_Man Nov 2

Mr Neutral Man aka "Howard Marks of REITs” on X

As with all over supply, time tends to take care of the problem. Im old enough to remember when SFH were generally thought if being in over supply forever Getting through this has taken wayyy longer than most folks imagined I’m waiting for $CPT to report. I kind of knew $MAA was gonna have tough/bad new/renewal mix I think it’s also part of their own fault where they push renewals too much

@aleabitoreddit Nov 2

Serenity on X

We've finally made it to November... holy crap October was brutal. Per Morgan Stanley data, the recent hedge fund de-grossing was the highest in the past 10 years. Exceeding the: - 2016 Chinese stock market crash - 2018 Volmageddon - March 2020 COVID crash - 2021 Archegos collapse / Evergrande crisis - 2022 bear market Goldman's prime brokerage desk reported on the 26th that it had also seen ~$20B of selling over 2 weeks vs. $23 billion during July's entire unwind. From $HOOD short interest data, from Sept -> Oct short float also increased from: $AAOI: 12.5% -> 19.84%+ $AEVA: 11% -> 25.64%+ $CBRS: 12.25% -> 20.12%+ $AEHR: 14.33% -> 17.93%+ $TSEM: 2.15% -> 4.73%+ $CIFR: 19.94% -> 25.39%+ $LWLG: 15.24% ->19.12%+ $OKLO: 17.72% ->22.93%+ $CRWV: 17.91% ->26.58%+ Substantial deleveraging coinciding with surging short interest, kinda looks like a July setup after Midterms? The recent rotation also kinda pushed names like $CRWD and cybersecurity to bubble valuations into hundreds of fwd P/E... Earnings on many AI capex + bottlenecks (not all like $INTC) looked strong: eg. $AXTI guided annualized Q4 = 23.7x P/E now, not including potential InP substrate hikes + capacity expansion that can compress it to single digits. - $SKHY (~$59B operating profit) casually making more than trillion dollar companies like Amazon ($28.1B operating profit), backed with 5Y LTAs. - No signs of $MSFT / $AMZN materially cutting capex, while $SPCX secures $40B more for GPU procurement. Slightly painful, but I think the AI capex trade (specifically photonics/memory) is a generational opportunity, with extreme volatility in between.

@BarbarianCap Nov 1 1 click

Barbarian Capital on X

7/n $BSP CEO @luke10ferrari on Joys of Compounding @Buhrman_Rick ⁠@PaulBuser⁠ Sounds like a very thoughtful operator, not that he needs my opinion https://t.co/Xwd1Namvk8

@finphysnerd Nov 1 3 clicks

Myles on X

I think $HONA looks pretty attractive here. Some execution risk, but this is fundamentally a fantastic business that is experiencing short term issues, while experiencing spinoff selling and hedge fund degrossing. I think it's hard to lose too much money here.

@DevinLaSarre Nov 1

Devin LaSarre on X

Newest out. Altria: Dutiful $MO $PM $BTI $IMBBY $STG $HAYPP https://t.co/eiMmjAwcRb

@partners_road Oct 31 1 click

RiverRoadPartners on X

2 wks into Q3 earnings, at least 4 semis flagged back-end constraints ($TXN, $ALGM, $PI, $NXPI), 2 noted customer push outs ($ALGM, $TER), $INTC noted higher memory prices caused demand destruction in PC’s & $LRCX couldn’t guide for upside b/c of a specialty component shortage.

@leevalueroach Oct 31 1 click

Lee Roach on X

I went to five different GameStop $GME stores today. Each store got Halloween Pokemon cards and if you came in and said trick-or-treat you got a free pack. All were out of inventory by the mid-morning. One store associate said Pokemon is still insanely hot and all of the cards were taken by adults and not a single kid got one. Wall Street is sleeping on the GameStop turnaround.

@convequity Oct 31

Convequity on X

Further thoughts on the $CBRS short-seller report I've already quote-tweeted this short-seller report, but I think it's quite funny. These days, a lot of tech short sellers seem to have a poor understanding of the technology itself. Perhaps they're better at timing market liquidity and identifying moments when sentiment is most vulnerable. Going back to the report's central claim, I think it's arguing that Cerebras hasn't adequately addressed the challenges of using a giant substrate to support an entire wafer, or of maintaining reliable connections across such a large chip. It's similar to the challenges facing $NVDA's Rubin Ultra. The idea is to connect four compute chips, with a combined size roughly eight times the reticle limit. At that scale, thermal expansion becomes a major issue, as does maintaining consistent communication between the chips. For Cerebras, these are certainly significant challenges. That's why it took so many years to turn the original idea into something that actually works. I would say the third-generation Wafer-Scale Engine, WSE-3, was the first generation to become commercially attractive. Earlier generations had lots of problems to work through, much like other chips, for example Google's TPU from v1 to v3. Even WSE-3 hasn't solved everything. One of the biggest remaining issues is parametric yield. The simplest way to explain it is that, when a chip spans an entire wafer, you can't guarantee that every part will operate reliably at the same high frequency. (Frequency is how many cycles per second the chip runs, so a higher frequency means more compute from the same silicon.) You therefore have to lower the operating frequency of the whole system. A normal chipmaker would cut the wafer into many separate dies and set each one's speed on its own. The argument is that those dies could then run at frequencies 50% higher, or perhaps even twice as high. Cerebras keeps the whole wafer as one chip instead, so it gives up some of the compute the same silicon could otherwise deliver. In return, it gets an enormous amount of compute and on-chip memory connected as one wafer-scale chip. Data moves between the cores over wiring on the wafer itself, rather than through slower, more power-hungry links between separate chips. That is what lets Cerebras serve AI models so quickly, especially for inference. That's the trade-off, and parametric yield has been a constraint for Cerebras. The workaround has been to lower the specifications and operating frequency, leaving enough headroom for the system to work reliably. But that also means leaving some of the wafer's potential unused, despite manufacturing it on an expensive, supply-constrained TSMC N5 process, and potentially on more advanced nodes in the future. That's why I find the latest CS-4 rack-level system, using the WSE-3T, interesting. My understanding is that WSE-3T uses the same wafer design as WSE-3 but delivers more than 50% higher performance. They've apparently found new ways to address the parametric-yield issue, allowing them to unlock frequency headroom they previously couldn't use reliably. It looks as though they're actively addressing the problem and making good progress. The earlier implementation may have been suboptimal, but even that version was already demonstrating commercial competitiveness. This is a common problem with short sellers today: they build a thesis around old information while the technology is evolving rapidly. They point to a technical limitation that may still exist, but fail to distinguish between a problem that makes a product unworkable and one that merely reduces its performance. Between roughly 2015 and 2021 or 2022, these issues may genuinely have been showstoppers. By late 2026, I don't think that's necessarily true anymore. They may still hold performance back somewhat, but they don't prevent the product from working. Meanwhile, Cerebras has the advantage of being the only company to have made a wafer-scale chip work. In my view, that benefit outweighs the remaining limitations. The short seller doesn't seem to recognize that distinction. I think this also explains why Oracle, AWS, OpenAI, and other companies have begun testing Cerebras chips this year. The technology seems to be reaching a point where these problems are largely, though not entirely, resolved. Previously, perhaps they were only halfway to a solution, and the product wasn't sufficiently practical. Now that they're much closer, large-scale commercial deployment becomes more attractive to customers. Of course, it remains to be seen how this plays out. They still haven't crossed the deployment threshold I have in mind, and once they do, we may see new problems emerge. For now, though, my sense is that these are well-known issues among people familiar with the technology. The short seller is largely repeating things that those people already understand. The more interesting question is the direction of progress: how quickly are the problems being resolved, and what does that mean for the product's competitiveness? The report doesn't really address that. The rest of the report seems to cover the usual short-seller material, raising fear, uncertainty, and doubt about adoption and other peripheral issues. But the central technical thesis strikes me as quite weak. It reminds me of previous short reports on companies such as $SMCI.