Author: Scott Buchanan

Chipmaker Stock Prices Explode: The Latest Bubble?

Scott Buchanan3 Comments

The share prices of many semiconductor chip companies have gone nearly vertical in the past month. Here are five-year charts for Micron (MU) and AMD, as of the close Monday:

Micron (MU) 5-Year Stock Chart

Advanced Micro Devices (AMD) 5-Year Stock Chart

Many analysts have been taken by surprise by the magnitude of the recent surge and prices. There has been no sudden, truly new news to drive this shift. It has been known for over a year that there is a huge shortage of memory chips, allowing Micron to charge high prices for its products.  But apparently the official quarterly announcement of earnings and projections substantiated that narrative. The bears have been claiming that memory chips are a cyclic business, where chip shortages are followed by building more manufacturing capacity, which inevitably leads to overcapacity and a crash in memory chip prices. It has happened repeatedly, and therefore the current Micron stock party would end in tears after a couple of years. But the bears have been beaten back to their caves for now. Micron was up another full 7% yesterday.

AMD, which specializes in central processing units (CPUs), also released good earnings and strong projections. But the real share price driver there seems to be the new narrative that the shift from the shift to agentic AI will require a higher ratio of CPUs to GPUs.  GPUs (graphic processing units) are the engines that do the core large language model (LLM) AI calculations. But apparently an increasing number of CPUs will be required to coordinate the activities of the GPUs:

AI agents—or the Agentic Era, as called by analysts—need more CPUs per GPU because they are responsible for the orchestration of AI workloads and the required data processing in order for the agent to accomplish its task, or, more simply, CPUs organize the steps of the workflow for the agent.    Traditional LLM models—not agents—required a CPU:GPU ratio of 1:4 to 1:8, but analysts anticipate this ratio to shift toward 1:2 or even 1:1 in the coming years.

All that to say demand for AMD‘s chips is projected to increase.

So far, so good. But apparently being swept up in the whirlwind of exhilaration is the share price for lowly Intel (INTC). Intel was the leading manufacturer of processor chips back in the day, but it missed the boat on GPUs and just cannot seem to execute at global standards. In recent years, Intel has mainly been famous for ever-slipping deadlines on producing high performing chips. Its earnings have been approximately zero for some time. The good news is it now has a foundry business. The bad news is that the foundry business loses around $2 billion a year. The foundry has pulled in a few large customers, and after their experience there, they all run screaming for the exits. But wait, there’s been an announcement that Apple may contract with Intel to produce some low-end chips. Whoopee!

Intel (INTC)  Five-year stock chart


Folks who look at technical behavior of stocks rather than the fundamentals of the business seem somewhat skeptical about the current surge. Terms like overbought are thrown around. I read an article claiming that hedging activities in the options market is creating an artificial, temporary demand for these high-flying stocks:

It is also fairly clear what has been driving these overbought conditions at the index level: aggressive call buying is creating a gamma squeeze across several stocks, such as Micron (MU). This occurs when aggressive call buying forces dealer hedging flows, resulting in purchases of the underlying stock. The more the stock rises, the more call buying tends to increase, and the cycle builds on itself.


My take on this spectacle

I can get the fundamental bull case in general for Micron stock. I bought into it about six months ago. Even that far back, it was clear that the demand for memory chips far outstripped the supply, so Micron could not help minting money for the next year or two. It was one of my fairly rare successes in stock picking. Sadly, I only bought a little bit, because I was influenced by many negative articles claiming that memory chips are a cyclic business, so this boom would end like all the previous Micron booms, with a glut and a crash.

There seems to be a solid bull case for AMD as well. For pitiful Intel, however, I see its price chart as a sign of market FOMO.

Where these stock prices go from here, I have no idea. My observation over the years is that this level of enthusiasm is usually followed eventually by, “What was I thinking?”, and a return to earth. However, in the meantime, tech stock prices often run up longer and further than I would have thought possible.

Usual disclaimer: Nothing here should be taken as advice to buy or sell any security.

100,000 Glyphosate Lawsuits: Why Roundup Does Not Kill Your Weeds Like It Used To

Scott Buchanan3 Comments

I don’t like wasting time bending over and pulling out weeds, one by one. Much more efficient to go squirt squirt and eliminate lots of weeds at a time. But I realized in the past year that the Roundup I spritzed on the weeds in my mulch beds and sidewalk cracks just wasn’t killing them like it used to. The weeds would shrivel a bit, but then many would bounce right back. So, when I went to Home Depot to buy some more this week, I looked at the ingredients on the label. What?? Where is the glyphosate? For decades, “Roundup” was synonymous with glyphosate.

Glyphosate has several desirable properties as an herbicide. You spray it on the leaves, and it kills the plants right down to the roots. However, it has minimal residual toxicity in the soil, so it is unlikely to kill any plants you did not spray, and you can replant quickly in a soil patch that you had cleared with glyphosate. Farmers love it, because you can buy genetically engineered strains of crops like corn that are immune to glyphosate, so you can spray your fields to kill weeds without harming standing crops.

The glyphosate story is much bigger than homeowners bending over to pull weeds. The chemical has become indispensable for current agriculture. Global glyphosate sales are about $10 billion per year, and its impact on crop productivity is enormous. A 2017 study (apparently not paid for by Monsanto) predicted dire effects of discontinuance:

World prices of all grains, oilseeds and sugar are expected to rise, especially soybeans (+5.4%) and rapeseed (+2%). The welfare impacts are mostly negative, with global welfare falling by $7,408 million per year. Land use changes will arise, with an additional cropping area of 762,000 ha, of which 53% derives from new land brought into cropping agriculture, including 167,000 of deforestation. These land use changes are likely to induce the generation of an additional 234,000 million kg of carbon dioxide emissions.

What’s not to like about glyphosate? Well, maybe it causes cancers in humans. This is a contested claim, and I don’t have the expertise to penetrate the arguments. Because glyphosate makers like Monsanto and its successors Bayer have deep pockets, lawyers on contingency have swarmed like killer bees to file lawsuits, over 100,000 of them, of which about 60,000 remain active globally.

National security issues have muddied the waters here. For instance, Robert F. Kennedy, Jr. led a landmark legal case against Monsanto in 2018, securing a $289 million jury verdict (later reduced on appeal to $20.4 million) for a school groundskeeper who developed non-Hodgkin’s lymphoma after prolonged exposure to Roundup. That case energized a bazillion further lawsuits. But now Kennedy is going along with the current administration’s position that it is strategically necessary to maintain production and responsible access to glyphosate: farmers demand it, and Bayer operates the only plant in the U.S. producing significant amounts of elemental phosphorus, which is a vital material for defense and, increasingly, for lithium batteries.

Naturally, Bayer denies that glyphosate is particularly harmful. The firm continues to sell the product to farmers and landscape professionals, but it has removed it from retail bottles of Roundup you see on Home Depot shelves, in an effort to reduce exposure for further litigation.

What have they substituted for good old glyphosate? I found a brew of three other chemicals. I can report reliably that this mixture is much less effective, especially on grasses and on well-established weeds. The Internet backs up my observations. The Iowa State garden extension has a great table of the real-world effects of all common herbicides.

So, what to do? For grasses in my backyard gravel patch, I am spraying multiple times. If that doesn’t work, I may try covering that area with a black tarp for a month to kill the grass. I have considered buying a propane flamethrower weeder, but that seems only effective on the same things the current wimpy Roundup kills (small/young broadleaf weeds).

For mulched areas, I am incentivized to keep up with fresh mulch to keep weeds from growing in the first place. For larger weeds, I have now found myself bending down low, grasping them close to the ground, and actually pulling them out by hand.

Placebos Really Work (Sometimes)

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This Simpsons episode has forever colored my gut response to placebos:

Crowd: We need a cure! We need a cure!

Dr. Hibbert: Ho ho ho. Why, the only cure is bedrest. Anything I give you would be a placebo.

Woman: [frantic] Where do we get these placebos?

Man: (points at truck) Maybe, there’s some in this truck!

(Crowd knocks over truck and a box of killer bees from it break out and attack the crowd.

No, but seriously – the placebo effect is real and important. If you take some pill which you believe might be efficacious (whether it is a sugar pill, or just some regular medication that happens to have no real biological, mechanistic relevance to your condition), voila, the symptoms (especially pain) may abate. This is nothing to sneer at – if you or a loved one is suffering serious pain, any relief is desperately welcome.

I think part of it has to do with our expectations of the pill itself, and part with the healing effect of another human being (doctor/nurse) expressing empathy and caring as he/she carefully dispenses the sugar pills.  Studies show that loving interactions with other people can help release endogenous opioids in the anterior cingulate cortex, which really do soothe pain. When a mother comforts her child who just fell down, or a friend listens sympathetically about your recent disappointment, there is actual brain chemistry going on.

In a placebo/drug trial, “people are exposed to an environment and procedures geared toward improving one’s health…You must visit a clinic at certain times and be examined by medical professionals. You undergo procedures or receive special medication. All this can profoundly impact how the body perceives symptoms because you feel both consciously and unconsciously that you are getting the attention and care needed to heal.”

Since I don’t think I can improve on it, here is an excerpt from an essay I prompted from Claude, asking about where placebos do and don’t work, and proposed mechanisms:

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~ ~ ~ ~ ~~ ~ ~ ~~ ~~ ~ ~ ~ ~ ~ ~ ~ ~

Where the Placebo Effect Appears

The placebo effect is most robustly documented in pain management. In landmark work by Levine, Gordon, and Fields (1978), patients who received a placebo after dental surgery experienced significant pain relief — relief that was partially reversed by naloxone, an opioid antagonist. This demonstrated that placebos could trigger the release of the body’s own endogenous opioids (endorphins), producing measurable analgesia through a real biochemical pathway.

Beyond pain, placebos have demonstrated consistent effects in:

  • Depression: Meta-analyses, including the influential work of Kirsch et al. (2008, PLoS Medicine), found that a substantial portion of antidepressant response in clinical trials was attributable to placebo, particularly in mild-to-moderate depression.
  • Parkinson’s disease: De la Fuente-Fernández et al. (2001, Science) used neuroimaging to show that placebo administration triggered genuine dopamine release in the striatum of Parkinson’s patients — a stunning demonstration of expectation-driven neurochemistry.
  • Irritable Bowel Syndrome (IBS): A 2010 study by Kaptchuk et al. (PLOS ONE) found that open-label placebos — patients told they were receiving sugar pills — still produced significant symptom reduction in IBS, suggesting conditioning effects persist even without deception.
  • Anxiety and asthma: Both show moderate, well-replicated placebo responses, particularly when tied to elaborate therapeutic rituals.

Where Placebos Fall Short

The placebo effect is not universal. It is largely absent or clinically negligible in:

  • Objective tumor biology: While patients may report feeling better, placebos do not shrink tumors, alter cancer markers, or extend survival in oncology trials.
  • Bacterial infections: No degree of expectation reduces bacterial load or alters antibiotic sensitivity.
  • Type 1 diabetes: Endogenous insulin production is not restored by belief.
  • Bone fracture healing: Structural tissue repair proceeds independently of psychological state.
  • Vision and hearing loss caused by structural damage: Nerve regeneration does not respond to suggestion.

The distinction broadly tracks a line between subjective, brain-mediated symptoms and structural or infectious disease. The nervous system can modulate what it controls; it cannot manufacture what biology has lost.

Proposed Mechanisms of Action

Several non-mutually exclusive mechanisms have been proposed:

1. Expectation and Predictive Processing
The leading cognitive account holds that the brain is a prediction machine. When a patient expects relief, the brain updates its internal model accordingly, and downstream perceptual and autonomic systems adjust to match the prediction. Tor Wager’s neuroimaging studies (Science, 2004) showed that placebo analgesia correlated with reduced activation in pain-processing regions (anterior cingulate, insula), consistent with top-down suppression of pain signals.

2. Endogenous Opioid and Dopamine Release
As noted above, placebo responses involve genuine neurotransmitter release. Opioid pathways mediate placebo analgesia; dopaminergic pathways mediate motor and mood improvements in Parkinson’s and depression. These are pharmacologically real events, not metaphors.

3. Classical Conditioning
Repeated pairing of a treatment context with active medication can produce conditioned responses where the context alone triggers the effect — even in the absence of the drug. This mechanism may explain why open-label placebos still work (Kaptchuk et al., 2010) and why placebo responses are stronger in patients with prior treatment experience.

4. Therapeutic Relationship and Meaning
The quality of the patient-clinician relationship independently predicts outcomes. Warmth, attentiveness, and ritual amplify placebo responses. This “meaning response,” as medical anthropologist Daniel Moerman (2002) termed it, suggests that healing is partly a social and symbolic act.

References

  1. Levine, J.D., Gordon, N.C., & Fields, H.L. (1978). The mechanism of placebo analgesia. The Lancet, 312(8091), 654–657.
  2. Kirsch, I., et al. (2008). Initial severity and antidepressant benefits: A meta-analysis of data submitted to the Food and Drug Administration. PLoS Medicine, 5(2), e45.
  3. De la Fuente-Fernández, R., et al. (2001). Expectation and dopamine release: Mechanism of the placebo effect in Parkinson’s disease. Science, 293(5532), 1164–1166.
  4. Kaptchuk, T.J., et al. (2010). Placebos without deception: A randomized controlled trial in irritable bowel syndrome. PLOS ONE, 5(12), e15591.
  5. Wager, T.D., et al. (2004). Placebo-induced changes in fMRI in the anticipation and experience of pain. Science, 303(5661), 1162–1167.
  6. Moerman, D.E. (2002). Meaning, Medicine and the ‘Placebo Effect’. Cambridge University Press.

Allbirds, Inc. Attempts Pivot from Making Wool Sneakers to AI Computing

Scott Buchanan1 Comment

A native New Zealander, Tim Brown had two separate ambitions: to become a professional soccer player and a designer. On the soccer (“football”, outside North America) front, he succeeded beyond expectations. He played on the New Zealand national team between 2004 and 2012, often as captain or vice-captain.  Brown executed a personal pivot in 2012. After retiring from soccer, he enrolled in the London School of Economics to learn the business skills needed to launch an idea he had been mulling for several years. This was a shoe made mainly of wool.

He wanted to give a boost to New Zealand’s declining sheep in industry (battered by competition from polyester textiles), and wanted to promote something more sustainable than the plasticky shoes that he was always being asked to endorse as a professional player. There seemed to be plenty of room in the half-billion dollar per year footwear industry for something more environmentally friendly.

Brown launched his idea on Kickstarter in 2014, raising over $100,000. He and his partner started selling the Allbirds Wool Runner in 2016. Their green vibe was perfect for that era, and their shoes became wildly popular among the Silicon Valley VC set. They were seen on Larry Page, Barack Obama, Leonardo DiCaprio, and a whole gaggle of Hollywood actors and actresses.

Allbirds expanded its product line, and opened brick and mortar stores on several continents. Allbirds went public in 2021, and its market value ran up to $4 billion. But then the novelty of wool shoes wore off, sustainability became less urgent, and it became widely known that these “Wool Runners” are too flimsy to actually run or exercise in. They are more like slippers, and folks outside of Hollywood or Silicon Valley were not eager to pay $150 for a pair of slippers. Also, better-capitalized competitors muscled into the sustainable footwear market. Sales slid down and down, management conflicts erupted, and founder Tim Brown left to pursue other interests. On April 1, Allbirds announced it was selling the remnants of its shoe business for an ignominious $39 million.

So far, the story is unremarkable – – as with so many other startups, idealistic founders have initial success, but eventually go under upon scale-up. But there is an interesting plot twist. Instead of just going chapter 7 BK, paying off creditors, and returning a few pennies to investors, the company is using the shell of its former business to generate capital and transform itself into a new AI venture of renting out computing centers for AI usage. I assume the managers wanted to keep their jobs as managers, and cooked up this scheme to traffic on the current AI hype.


Apparently, these guys know nothing about GPU centers, so they’ll have to hire folks with expertise. Some unknown investor is backing them to the tune of $50 million, but they will have to raise much more than that to compete in the AI server business. That will horribly dilute current stockholders. They are directly competing with much better-capitalized behemoths like CoreWeave and Oracle, that can raise money on better terms. No moat, no expertise, almost no capital. But, hey, it’s AI, and so the company stock BIRD soared 600% on the news of the computing pivot.

I give them modest odds of succeeding bigly, but sometimes a mission pivot like this does come off. I’m thinking of the 1960’s when Berkshire Hathaway, facing declining earnings from its core textile business, under the leadership of Warren Buffett shifted into insurance. That generated the “float” that then enabled the purchase of other profitable businesses. We shall see if Allbirds (soon to be “NewBird”) management can likewise preside over such a seismic business shift.

Claude Mythos Is Such a Dangerous Hacker Engine That Anthropic Has Withheld Broad Release

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The latest AI model from Anthropic is so powerful that they don’t dare release it to the public. It is such a threat that Jay Powell and Scott Bessant summoned the major bank CEOs to a meeting last week to warn them about it. In line with Anthropic’s “helpful, honest, and harmless” motto, they have released it only to their Project Glasswing partners. These are organizations like AWS, Apple, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks, who have been granted access to the model to identify and patch vulnerabilities in critical software.

Mythos is designed to identify and exploit vulnerabilities in software systems when prompted. Its specialty is identifying critical software vulnerabilities and bugs, but it can also assemble sophisticated exploits.

What makes Mythos particularly unsettling is that its most dangerous capabilities were not deliberately engineered. Anthropic’s team made it clear that they did not explicitly train Mythos to have these capabilities. Instead, they “emerged.”

Internal testing revealed that Mythos has already uncovered thousands of weak points in “every major operating system and web browser.” The implications are disturbing. Claude Mythos has autonomously discovered thousands of zero-day vulnerabilities in major operating systems and web browsers— flaws that human security researchers, working for years, had never detected. (see also here and here for examples).

Mythos can rapidly uncover hidden flaws in the codes of organizations and software development firms, but it also raises the fear that attackers could find those vulnerabilities first. Much of the underlying software that Mythos can scan supports banking, retail, airlines, hospitals, and critical utilities. Regulators worry that if Mythos, or models like it, fell into the wrong hands, “systemically important” banks and even entire financial networks could be compromised before institutions even knew they were exposed.

Anthropic launched Project Glasswing in April 2026 to collaborate with tech giants and banks to identify and fix vulnerabilities before they can be exploited.   This year, organizations should expect a large influx of AI-discovered hack points in critical software. The game plan is to use AI tools to patch the vulnerabilities it discovers. Your venerable legacy system is no longer safe. What AI can expose, it can also fix. We hope.

Ray Kurzweil predicted The Singularity (when artificial intelligence growth accelerates beyond human control) would arrive in 2045, but we might be closing in on it ahead of schedule.

Oops: Anthropic Accidently Leaked the Entire Code for Its “Claude Code” Program

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One of Anthropic’s biggest wins has been its wildly-popular Claude Code program, that can do nearly all the grunt work of programming. Properly prompted, it can build new features, migrate databases, fix errors, and automate workflows.

So, it was big news in the AI world last week when an Anthropic employee accidently exposed a link that allowed folks to download the source code for this crown jewel – – the entire code, all 512,000 lines of it, which revealed the complete logic flow of the program, down to the tiniest features. For instance, Claude Code scans for profanity and negative phrases like “this sucks” to discern user sentiment, and tries to adjust for user frustration.

Gleeful researchers, competitors, and hackers promptly downloaded zillions of copies. Anthropic issued broad copyright takedown requests, but the damage was done. Researchers quickly used AI to rewrite the original TypeScript source code into Python and Rust, claiming to get around copyright laws on the original code. Oh, the irony: for years, AI purveyors have been arguing that when they ingest the contents of every published work (including copyrighted works) and repackage them, that’s OK. So now Anthropic is tasting the other side of that claim.

The leak has been damaging to Anthropic to some degree. Competitors don’t have to work to try to reverse engineer Claude Code, since now they know exactly how it works. Hackers have been quick to exploit vulnerabilities revealed by the leak. And Anthropic’s claim to be all about “Safety First” has been tarnished.

On the other hand, the model weights weren’t exposed, so you can’t just run the leaked code and get Claude’s results. Also, no customer data was revealed. Power users have been able to discern from the source how to run Claude Code most advantageously. This YouTube by Nick Puru discussed such optimizations, which he summarized in this roadmap:

There have actually been a number of unexpected benefits of the leak for Anthropic. Per AI:

Brand resonance and community engagement have surged, with some observers calling the incident “peak anthropic energy” that generated significant hype and validated the product’s technical impressiveness.  The leak has acted as a massive free marketing campaign, reinforcing the narrative of a fast-moving, innovative company while bouncing the brand back among developers despite the security lapse. 

Accelerated ecosystem adoption and bug fixing are also potential benefits, as the exposure allowed engineers to dissect the agentic harness and create open-source versions or “harnesses” that keep users within the Anthropic ecosystem. Additionally, the public scrutiny likely helps identify and patch vulnerabilities faster, while the leaked source maps provide a roadmap for competitors to build “Claude-like” agents, potentially standardizing the market around Anthropic’s architectural patterns.

The leak also revealed hidden roadmap features that build anticipation, such as:

  • Kairos: A persistent background daemon for continuous operation. 
  • Proactive Mode: A feature allowing the AI to act without explicit user prompts. 
  • Terminal Pets: Playful, personality-driven interfaces to increase user engagement.

Because of these benefits, conspiracy theorists have proposed that Anthropic leaked the code on purpose, or even (April Fools!) leaked fake code. Fact checkers have come to the rescue to debunk the conspiracy claims. But in the humans vs. AI competency debate, this whole kerfuffle doesn’t make humans look so great.

WW II Key Initiatives 4: Building Hundreds of Small, Slow, But Cheap Ships to Counter the U-Boat Threat

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This is the fourth in a series of occasional blog posts on individual initiatives that made a strategic (not just tactical) difference in the course of the second world war. World War II was not only the biggest, bloodiest conflict, in human history. It played a definitive role in giving us the world we have today. Everyone can find something to complain about in the current state of affairs, but think for a moment what the world would be like if the Axis powers had prevailed.

Winston Churchill’s biggest single worry in WWII was that German submarines (U-boats) would sink enough cargo and troop ships to cut Britain off from America and other allied countries. The standard anti-submarine weapon for the stormy Atlantic was the full-sized destroyer. Destroyers were fast, largely weather-proof, and bristled with guns and depth charge launchers. Unfortunately, building a destroyer took a lot of resources and time, particularly for the state-of-the-art steam turbine engine. There was just no way in 1939-1942 to produce enough destroyers to cover all sides of every convoy in the Atlantic.

The British Admiralty knew they needed some sort of small ship that could be readily produced by civilian shipyards, but they did not know what exactly that would look like. It fell to William Reed, a naval architect at Smith’s Dock Company, to propose a workable design. He based his design on a successful whaling ship, which was just large enough to survive the Atlantic weather. It was powered by a low-tech triple expansion steam piston engine. This Victorian-era sort of engine could be built by even small shipyards. The resulting boat, called a corvette, was small (200 ft long), slow (16 knots), rolled horribly in the waves, and was lightly armed (one forward 4-inch deck gun for surface duels, and simple roll-off racks for depth charges at the stern). But it was good enough for its one mission, which was to sink or pin down U-boats trying to attack a convey.

By the end of January 1940, 116 ships were building or on order to this initial design. Over 200 were eventually built in UK and Canadian shipyards. Twenty-two of these Flower-class corvettes were sunk by enemy action, and the conditions for their crews were miserable, but they are credited with tipping the balance of the Battle of the Atlantic, which was a crucial phase of WWII.

For his contributions, Reed was appointed an Officer of the Order of the British Empire.

How to Install Drywall

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Nearly every interior wall and ceiling in every home in America is covered with sheetrock = drywall = gypsum board. Sheetrock (a brand name for drywall) consists of an interior layer of rigid gypsum (a mineral composed of calcium sulfate dihydrate) plus some additives, with outside layers of strong paper or fiberglass. It normally comes in 4 ft x 8 ft sheets.

Normal houses have a framework of mainly 2×4 or larger wood lumber. Each wall has vertical 2×4 studs, spaced every 16”. Sheetrock is trimmed to size, and nailed or (these days) screwed into the studs.

That is the theory, anyway.

I have never done this stuff at large scale before, other than clumsily patching occasional small dings in a wall. A little while ago, I got to experience the process, hands-on. I was part of a team that helped someone whose basement had flooded. We cut out the lower ~4 ft of drywall, and replaced it with fresh drywall.

First, how to you cut drywall? A long, straight cut is accomplished by drawing a straight line and cutting along it, all the way through one layer of the facing paper. Then you hang the drywall sheet on the edge of a table, and crack the interior gypsum layer. Then you cut the other side of the paper. The end result of such a cut is like this:

Typically, you install drywall on the ceiling first. Then the top 4 ft of the walls, then the bottom 4 ft of the walls. You butt the pieces close to each other. For the lowest piece of drywall, you insert a curved metal wedge under it, and step on the wedge with your foot to lift that drywall piece to butt its top edge up against the upper piece. If you look carefully near the middle of the following photo, you can see the red wedge I used to jack up that small lower piece of drywall. It’s OK to leave a gap between the floor and the lower edge of the bottom drywall, since that gap will be covered by baseboard.

This was in a bathroom. I cut the lower green pieces with a little hand power saw, and screwed them into the studs, using the green and black driver visible on the stand in the left foreground.

The next two photos are before and after of a bedroom wall, again showing the bottom course of sheetrock we installed.

Filling in Cracks and Holes

As you can see, at this stage, there are like ¼” cracks between the installed sheets of sheetrock, and the mounting screw holes are visible. These imperfections are filled in with goo called joint compound, or “mud.” The mud is applied with a “knife” like this:

Cracks are covered with paper or fiberglass tape, with mud smeared over the tape. Typically, three layers of mud are needed to achieve perfect, smooth coverage. Each layer must dry hard before applying the next layer. Each layer may be sanded lightly as needed.

 A key technique is to tilt the knife so the mud is maybe 1/16” thick over the tape or over a screw, but taper the mud to zero thickness on the wall away from the tape or screw. This feathering is essential; if your mud layer ends with appreciable thickness instead of feathering, you have to do a lot of sanding to get a smooth blending into the plain drywall at that edge. Pro tip: carefully stir more water into the joint compound as needed to keep it wet and flowing, especially for overnight storage. This video from Vancouver Carpenter displays mudding technique.

That is mainly it. For perspective and confidence building, it is helpful to work with an expert, as I was able to do.

A Bull Case for Tech Stocks

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Negative headlines tend to get more attention than bland positive titles. We have seen a lot of angst in the past few months over the massive capex spend by big tech companies, with questions over whether there will be adequate returns on these investments.

There was a genuine untethered bubble in tech stocks circa 1997-2000. Companies with no earnings and no moats were given billion-dollar valuations, on the strength of a business plan sketched on a cocktail napkin. After the brutal bursting of that bubble, tech stocks repriced and then steadily strengthened for the next 25 years.

Nevertheless, it seems there is always some negative narrative to be found regarding tech stock valuations and prospects. Seeking Alpha author Beth Kindig writes that investors who were spooked by all those bubble warnings lost out big time:

Investors have been hearing “tech bubble” warnings for more than a decade — but instead of collapsing, the Nasdaq‑100 has gained 550%. If we look back ten years ago to 2015, headlines such as “Sell everything! 2016 will be a cataclysmic year” confronted investors with calls for an imminent recession. The bears made repeated claims that a “tech bubble” was about to burst with some of the world’s most prominent venture capitalists drawing parallels to the dot-com era.

What followed tells a very different story, with not only the Nasdaq-100 up 550% over a 10-year period but also high-flying stocks like Shopify returning as much as 5200% and Nvidia returning 22,000% over the same period.

It’s true that capturing those gains does not come easy. Investors had to hold through five drawdowns that were greater than 20%, including two declines greater than 30%, while tuning out a constant stream of bearish commentary – often from reputable sources – proclaiming the long-awaited tech bubble has finally “popped.” Despite these strong convictions, the long-term trend remained intact.

She presented this graphic which illustrates many of the negative headlines over the past decade:

While she acknowledges that traditional cloud computing applications are slowing in growth rates, and there will be general market price volatility, she contends that AI is still in an acceleration phase:

The dot-com era was defined by oversupply and fragile fundamentals; today’s AI buildout is being led by the world’s strongest operators, backed by real revenues and profits, and constrained by hard limits in compute, memory, networking, and power.

The more important question isn’t whether we’ll see a pullback — it’s where we are in the cycle. AI is still transitioning from the training phase into the inference phase, where monetization will accelerate and the “capex with no revenue” narrative will begin to fade. In other words, the loudest bubble debates are arriving before the most important revenue engine fully turns on.

Those of us who are long tech stocks hope she is correct.

How a Protective Options Collar Cushioned a Loss in Korean Stock Fund EWY

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After being convinced by a series of favorable articles, I bought a few shares last month of the EWY fund, which holds shares of major South Korean companies. The narrative seemed compelling: the vast production of compute processing chips for AI has led to a structural supply shortage of fast memory chips. South Korean firms excel in making these chips, and so high, growing profits seemed assured. What could possibly go wrong?

What I didn’t know was that thousands of other retail investors were thinking the exact same thing, and hence had bid the price of EWY up to possibly unreasonable levels. Somehow, my bullish analysts missed that point. In particular, the South Korean market is driven by an unusually high level of margin trading, where investors borrow money on margin to buy shares. A market drop leads to margin calls, which leads to forced selling, which really crashes prices.

The other thing I did not know was that, two days after my purchase, the attacks on Iran would commence. Oops. Among other things, this would drive up the world price of oil, which impacts energy importers like South Korea. This seems to have been the trigger for the sharp stock drop.

Here is the six-month price chart for EWY:

As it happened, I bought pretty much at the top, and as of Monday midday when I am writing this, EWY was down about 17%. That doesn’t look like much of a drop on the chart, because of the long run-up to this point, but it is an unpleasant development if you just bought in two weeks ago.

Fortunately, when I bought the EWY shares, I set up a protective options collar, since this was not a high conviction buy. First, I bought a put with a strike price about 7% below my purchase price, which would limit my maximum loss on the EWY shares to 7%. A problem is that this put cost serious money (about 11% of the share price), so my maximum loss could actually be 7% plus 11% = 18%. Therefore, I offset nearly all the cost of the put by selling a call with a strike price about 17% above the current EWY share price. That meant that I could profit from a rise in EWY share price by up to 17%, while being protected against a drop of more than 7%. That seemed like a favorable asymmetry (7% max loss vs 17% max gain).

This arrangement (buying a protective put to limit downside, financed by selling a call which limits upside) is called an options “collar”. I’d rather accept a limited upside than have to worry about doing clever trading to mitigate a big loss.

As of Monday, my collar was working well to protect the overall position. As might be expected, the value of my put increased, with the drop in EWY share price. But also, the value of my call decreased, which further helps me, since I am short that call. The net result was that about 75% of the loss in the stock price was compensated by the changes in values of the two options.

This is just a small, experimental position, but it was nice to see practical outcomes line up with theory.

Disclaimer: As usual, nothing here should be considered advice to buy or sell any security.