Scott Buchanan

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

March 31, 2026March 31, 2026Scott BuchananLeave a comment

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

March 24, 2026March 24, 2026Scott Buchanan1 Comment

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

March 17, 2026March 17, 2026Scott BuchananLeave a comment

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

March 10, 2026March 10, 2026Scott BuchananLeave a comment

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.

Autism Is Largely Genetic (Not Environmental)

March 3, 2026March 3, 2026Scott Buchanan1 Comment

Autism is a condition that can cause enormous anxiety and grief, especially for parents of autistic children. The economic implications are also considerable. A 2021 study by Blaxill, et al., estimated the annual costs to society of autism in the U.S. to be $223 billion in 2020, $589 billion in 2030, $1.36 trillion in 2040, and an astonishing $5.54 trillion by 2060.

The rising diagnosis rates are sometimes attributed to changes in environmental factors or diets. It is obviously essential to get the science right on this. Here I will summarize an article by epidemiologist Mark Strand, “Understanding Autism Spectrum Disorder Epidemiologically and Theologically”. This article was published on the Biologos web site on January, 2026. The author addresses the medical aspects sand also the moral aspects. Upfront disclaimer: I have no expertise in this area; I am just trying to faithfully convey the scientific consensus.

The Myth of the “Autism Epidemic”

The article begins by addressing a common misconception: that autism is experiencing a sudden, alarming surge in cases—an “epidemic.” This idea gained traction when the current administration announced a “massive testing and research effort” to identify environmental causes behind the rise. But as the author explains, this framing is scientifically inaccurate.

Autism is not an acute condition like strep throat or a viral outbreak. It’s a lifelong neurodevelopmental disorder that emerges during early brain development, typically between ages two and four. Unlike infectious diseases that appear suddenly and resolve quickly, autism is chronic and complex. The term “epidemic” refers to a rapid, atypical increase in cases—something that doesn’t align with the actual data.

In 2022, the CDC’s Autism and Developmental Disabilities Monitoring (ADDM) Network reported 32.2 cases of ASD per 1,000 children. This is massive (fourfold) increase from two decades ago. But this rise isn’t due to a sudden environmental trigger. Instead, it reflects broader diagnostic criteria, increased public awareness, better screening practices, and greater access to services:

While this increase may seem alarming at first glance, it is widely accepted that it largely reflects changes in the definition used for ASD and the importance placed on ASD by society and educational systems.

Since it was first recognized as a condition in the Diagnostic and Statistical Manual of Mental Disorders (DSM), the definition for ASD has been broadened several times. This included combining a cluster of related neurological disorders into one disorder, relaxing the age of onset, and better classifying the presentation of autism in girls and children of color, leading to more accurate, but higher, numbers.

States like California and Pennsylvania, which have robust early intervention systems and strong Medicaid coverage for autism services, report the highest prevalence rates.

Genetics Over Environment: The Scientific Consensus

One of the most critical points the article makes is that autism is primarily genetic. Twin studies show that if one identical twin has autism, the other twin has a 60% to 90% chance of also having it. A large Swedish study of over 37,000 twins found that 83% of autism cases could be attributed to genetics.

While environmental factors may play a role, the evidence is far from conclusive. The article debunks popular myths—like the claim that acetaminophen (Tylenol) use during pregnancy causes autism. High-quality studies, including one of 2.5 million children in Sweden with sibling controls, found no link between prenatal acetaminophen use and autism risk. Similarly, the idea that vaccines cause autism has been thoroughly discredited by decades of rigorous research.

The real danger lies in chasing unproven causes—a practice that wastes resources and distracts from meaningful science. Instead, researchers should focus on gaps in knowledge using the scientific method: building on what we know, forming new hypotheses, and testing them rigorously.

The Spectrum of Experience: Diversity, Not Deficit

Autism is not a monolithic condition. It exists on a spectrum, ranging from individuals with significant support needs to those with high intelligence and exceptional skills.

The article highlights some striking statistics:

14% of autistic individuals graduate from college, compared to 32% of the general population.
Among college graduates with autism, 34.3% major in STEM fields, significantly higher than the 22.8% in the general population.

These numbers challenge the harmful stereotype that autistic people are universally disabled or burdensome. Many autistic individuals thrive in science, technology, engineering, and mathematics—fields that value pattern recognition, attention to detail, and deep focus.

Yet, challenges remain. Social communication difficulties, restricted interests, and repetitive behaviors can be isolating. Early intervention—especially for those with moderate to mild autism—can make a meaningful difference in socialization and quality of life.

A Call for Grace, Truth, and Inclusion

The article concludes by noting that autism is not a tragedy, but a part of human diversity. It calls on society at large to respond with truth, grace, and care—not fear or stigma.

The article notes: “It is not a good use of resources to repeat studies on well-established scientific evidence or chase popular beliefs about supposed causes.” Rather than searching for a single cause to “eliminate,” we should focus on understanding, supporting, and empowering autistic people. This includes investing in early screening, improving access to therapy, and promoting inclusive education and employment.

The rise in autism diagnoses is not a crisis to panic about—it’s a call to do better with better science, better policies, and better compassion. By grounding our understanding in data, embracing neurodiversity, and responding with love, we can build a world where autistic individuals are not just accepted—but valued.

Sleigh or Sled Shovels: Move Lots of Snow with No Lifting

February 24, 2026Scott Buchanan2 Comments

Now that we have your attention (if you just got buried in a blizzard yesterday), let’s talk about shoveling snow. Everyone knows how a standard snow shovel works. You bend down, with one hand on the end of the handle and the other hand halfway along the handle, you shove forward, load up the shovel blade, then (Ooof!) lift it up and throw the snow where it needs to go. For many of us, this action uses muscles and joints that are not conditioned for it. Fun facts: every year some 100 Americans die from shoveling snow, and another 11,000 or so end up in the emergency room.

Is there a better way? Well, a powered snowblower can work. But that doesn’t fit everyone’s situation. It turns out there is a better way to manually shovel snow, that fits many (not all) situations.

As I was reading about “electric snow shovels” (more on that another time), I ran across mention of “sleigh shovels” or “sled shovels” or “snow scoops.” Apparently, they are very widely used by Canadians and Alaskans, who ought to know something about snow. A genius aspect of these shovels is that you never have to lift them.

Here is a picture of a 24” Garant brand sled shovel:

Source: Ace Hardware

Here’s how they work: Start with the position shown, shove it forward (you get to use both hands out in front of you, in an ergonomically good position), till the scoop is largely filled with snow. Then, tilt it back a little, and push this load forward, sledding along until you get to the edge of the driveway. Keep pushing it another several feet, out onto the lawn. Then dump the snow off the shovel by a quick shove forward and a sudden jerk back, to pull the shovel out from under the snow. Plan your dumping points so as to get a gradual ridge beside the driveway, not a narrow, high ridge right at the edge.

Here is a 47-second video demo, on a small scale.

Take a quick look at 1:40 – 3:40 (two minutes) of this video to see a more challenging situation (deep snow, big existing ridge on edge). This shows that one scoop shovel-full is equivalent to more than three regular shovel-fulls, and this snow is expelled from the driveway with NO LIFTING. It’s beautiful! Here are two screen shots from this video:

Garant seems to be the most well-established brand here. ACE hardware (see photo above) is selling them for $70. On Amazon, I see a Garand model being sold for an eye-watering $266, maybe scalping prices for the latest blizzard. That is a lot of money for a plastic scoop with a metal handle. You can probably do better by shopping elsewhere or at a different time.

I am tempted to get one, but I don’t have a wide driveway with grassy dumping areas on the sides. I have to shovel mainly steps and narrow sidewalks, often with wet, slushy, not super deep snow. Sleigh shovels can work in these situations, but their advantages are muted, compared to the deep powdery snow found in colder regions.

But if I were living in Boston or Providence or New York, a sleigh shovel would be mighty handy right now.

Broad Slump in Tech and Other Stocks: Fear Over AI Disruption Replaces AI Euphoria

February 17, 2026February 17, 2026Scott BuchananLeave a comment

Tech stocks (e.g. QQQ) roared up and up and up for most of 2023-2025, more than doubling in those three years. A big driving narrative was how AI was going to make everything amazing – productivity (and presumably profits) would soar, and robust investments in computing capacity (chips and buildings), and electric power infrastructure buildout, would goose the whole economy.

Will the Enormous AI Capex Spending Really Pay Off?

But in the past few months, a different narrative seems to have taken hold. Now the buzz is “the dark side of AI”. First, there is growing angst among investors over how much money the Big Tech hyperscalers (Google, Meta, Amazon, Microsoft, plus Oracle) are pouring into AI-related capital investments. These five firms alone are projected to spend over $0.6 trillion (!) in 2026. When some of this companies announced greater than expected spends in recent earning calls, analysts threw up all over their balance sheets. These are just eye-watering amounts, and investors have gotten a little wobbly in their support. These spends have an immediate effect on cash flow, driving it in some cases to around zero. And the depreciation on all that capex will come back to bite GAAP earnings in the coming years, driving nominal price/earnings even higher.

The critical question here is whether all that capex will pay out with mushrooming earnings three or four years down the road, or is the life blood of these companies just being flushed down the drain? This is viewed as an existential arms race: benefits are not guaranteed for this big spend, but if you don’t do this spending, you will definitely get left behind. Firms like Amazon have a long history of investing for years at little profit, in order to achieve some ultimately profitable, wide-moat quasi-monopoly status. If one AI program can manage to edge out everyone else, it could become the default application, like Amazon for online shopping or Google/YouTube for search and videos. The One AI could in fact rule us all.

Many Companies May Get Disrupted By AI

We wrote last week on the crash in enterprise software stocks like Salesforce and ServiceNow (“SaaSpocalypse”). The fear is that cheaper AI programs can do what these expensive services do for managing corporate data. The fear is now spreading more broadly (“AI Scare Trade”); investors are rotating out of many firms with high-fee, labor-driven service models seen as susceptible to AI disruption. Here are two representative examples:

Wealth management companies Charles Schwab and Raymond James dropped 10% and 8% last week after a tech startup announced an AI-driven tax planning tool that could customize strategies for clients
Freight logistics firms C.H. Robinson and Universal Logistics fell 11% and 9% after some little AI outfit announced freight handling automation software

These AI disruption scenarios have been known for a long time as possibilities, but in the present mood, each new actual, specific case is feeding the melancholy narrative.

All is not doom and gloom here, as investors flee software companies they are embracing old-fashioned makers of consumer goods and other “stuff”:

The narrative last week was very clearly that “physical” was a better bet than “digital.” Physical goods and resources can’t be replaced by AI like digital goods and services can be at an alarming rate

As I write this (Monday), U.S. markets are closed for the holiday. We will see in the coming week whether fear or greed will have the upper hand.

SaaSmageddon: Will AI Eat the Software Business?

February 10, 2026February 10, 2026Scott BuchananLeave a comment

A big narrative for the past fifteen years has been that “software is eating the world.” This described a transformative shift where digital software companies disrupted traditional industries, such as retail, transportation, entertainment and finance, by leveraging cloud computing, mobile technology, and scalable platforms. This prophecy has largely come true, with companies like Amazon, Netflix, Uber, and Airbnb redefining entire sectors. Who takes a taxi anymore?

However, the narrative is now evolving. As generative AI advances, a new phase is emerging: “AI is eating software.” Analysts predict that AI will replace traditional software applications by enabling natural language interfaces and autonomous agents that perform complex tasks without needing specialized tools. This shift threatens the $200 billion SaaS (Software-as-a-Service) industry, as AI reduces the need for dedicated software platforms and automates workflows previously reliant on human input.

A recent jolt here has been the January 30 release by Anthropic of plug-in modules for Claude, which allow a relatively untrained user to enter plain English commands (“vibe coding”) that direct Claude to perform role-specific tasks like contract review, financial modeling, CRM integration, and campaign drafting. (CRM integration is the process of connecting a Customer Relationship Management system with other business applications, such as marketing automation, ERP, e-commerce, accounting, and customer service platforms.)

That means Claude is doing some serious heavy lifting here. Currently, companies pay big bucks yearly to “enterprise software” firms like SAP and ServiceNow (NOW) and Salesforce to come in and integrate all their corporate data storage and flows. This must-have service is viewed as really hard to do, requiring highly trained specialists and proprietary software tools. Hence, high profit margins for these enterprise software firms.

Until recently, these firms been darlings of the stock market. For instance, as of June, 2025, NOW was up nearly 2000% over the past ten years. Imagine putting $20,000 into NOW in 2015, and seeing it mushroom to nearly $400,000. (AI tells me that $400,000 would currently buy you a “used yacht in the 40 to 50-foot range.”)

With the threat of AI, and probably with some general profit-taking in the overheated tech sector, the share price of these firms has plummeted. Here is a six-month chart for NOW:

Source: Seeking Alpha

NOW is down around 40% in the past six months. Most analysts seem positive, however, that this is a market overreaction. A key value-add of an enterprise software firm is the custody of the data itself, in various secure and tailored databases, and that seems to be something that an external AI program cannot replace, at least for now. The capability to pull data out and crunch it (which AI is offering) it is kind of icing on the cake.

Firms like NOW are adjusting to the new narrative, by offering pay-per-usage, as an alternative to pay-per-user (“seats”). But this does not seem to be hurting their revenues. These firms claim that they can harness the power of AI (either generic AI or their own software) to do pretty much everything that AI claims for itself. Earnings of these firms do not seem to be slowing down.

With the recent stock price crash, the P/E for NOW is around 24, with a projected earnings growth rate of around 25% per year. Compared to, say, Walmart with a P/E of 45 and a projected growth rate of around 10%, NOW looks pretty cheap to me at the moment.

(Disclosure: I just bought some NOW. Time will tell if that was wise.)

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

After the Crash: Silver Clawing Back Up After Epic Bust Last Week

February 3, 2026February 3, 2026Scott BuchananLeave a comment

A month ago (red arrow in 5-year chart below), I noticed that the price of silver was starting into a parabolic rise pattern. That is typical of speculative bubbles. Those bubbles usually end in a bust. Also, the rise in silver price seemed to be mainly driven by retail speculators, fueled by half-baked narratives rather than physical reality.

Five-year chart of silver prices $/oz, per Trading View

So I wrote a blog post here last month warning of a bubble, and sold about a quarter of my silver holdings. (I also initiated some protective options but that’s another story for another time.) I then felt pretty foolish for the next four weeks, as silver prices went up and up and up, a good 40% percent over the point I initially thought it was a bubble. Maybe I was wrong, or maybe the market can stay irrational longer than you can stay solvent, per J. M. Keynes.

When the crash finally came, it was truly epic. Below is a one-month chart of silver price. The two red lines show silver price at the close of regular trading on Thursday, January 29 (115.5 $/oz), and at the close of trading on Friday, January 30 (84.6 $/oz):

This is a drop of nearly 30% in one day, which is a mind-boggling move for a major commodity. Gold got dragged down, too:

These aren’t normal moves. Over roughly the past 25+ years (through 2025), gold’s price has changed by about 0.8% per day on average (in absolute percentage terms). Silver, being more volatile, has averaged around 1.4–1.5% per day. If you’re scoring at home, that’s about a 13 Sigma move for Gold and 22 Sigma move for Silver! You’re witnessing something that shouldn’t happen more than once in several lifetimes…statistically speaking. Yet here we are.

After the fact, a number of causes for the crash were proposed:

The nomination of Kevin Warsh as the next Federal Reserve Chair. Warsh is perceived as a hawkish policymaker, leading investors to expect tighter monetary policy, higher interest rates, and a stronger U.S. dollar—all of which reduce the appeal of non-yielding assets like silver.

Aggressive profit-taking after silver surged over 40% year-to-date and hit record highs near $121 per ounce.
Leveraged positions in silver futures were rapidly unwound as prices broke key technical levels, triggering stop-loss orders and margin calls.
CME margin hikes (up to 36% for silver futures) increased trading costs, forcing traders to cut exposure and accelerating the sell-off.
Extreme speculation among Chinese investors, leading the Chinese government to clamp down on speculative trading. (And presumably Chinese solar panel manufacturers have been complaining to the government about high costs for silver components).

What happens next?

Silver kept falling to a low of 72.9 $/oz in the wee hours of February 2, a drop of 40% percent from the high of 120.8 on Jan 26. However, it looks to my amateur eyes like the silver bubble is not really tamed yet. For all the drama of a 22-sigma crash one day crash, about all that did was erase one months’ worth of speculative gains. The charts above are showing that silver is clawing its way right back up again. It is very roughly on the trend line of the past six months, if one excludes the monster surge in the month of January.

There is a saying among commodities traders, that the cure for high prices is high prices. This means that over time, there will be adjustments that will bring down prices. In the case of silver, that will include figuring out ways to use less of it, including recycling and substitution of other metals like copper and aluminum. However, my guess is that the silver bulls feel vindicated by the price action so far, and will keep on buying at least for now.

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

Economic Impacts of Weather Apps Exaggerating Storm Dangers

January 27, 2026January 27, 2026Scott Buchanan1 Comment

Snowmageddon!! Over 20 inches of snow!!! That is what we in the mid-Atlantic should expect on Sat-Sun Jan 24-25 according to most weather apps, as of 9-10 days ahead of time. Of course, that kept us all busy checking those apps for the next week. As of Wednesday, I was still seeing numbers in the high teens in most cases, using Washington, D.C. as a representative location. But my Brave browser AI search proved its intelligence on Wednesday by telling me, with a big yellow triangle warning sign:

Note: Apps and social media often display extreme snow totals (e.g., 23 inches) that are not yet supported by consensus models. Experts recommend preparing for 6–12 inches as a realistic baseline, with the potential for more.

“Huh,” thought I. Well, duh, the more scared they make us, the more eyeballs they get and the more ad revenue they generate. Follow the money…

Unfortunately, I did not log exactly who said what when last week. My recollection is that weather.com was still predicting high teens snowfall as of Thursday, and the Apple weather app was still saying that as of Friday. The final total for D.C. was about 7.5 inches for winter storm Fern. In fairness, some very nearby areas got 9-10 inches, and it ended up being dense sleet rather than light fluffy snow. But there was still a pretty big mismatch.

Among the best forecasters I found was AccuWeather. They showed a short table of probabilities that centered on (as I recall) 6-10”, with some chances for higher and for lower, that let you decide whether to prepare for a low probability/high impact scenario. It seems that the Apple weather app is notoriously bad: instead of integrating several different forecast models like some other apps (and like your local talking head meteorologist), it apparently just spits out the results of one model:

The core issue is that many weather apps, including Apple Weather, display raw data from individual forecast models without the context and analysis that professional meteorologists provide. While meteorologists at the National Weather Service balance multiple computer models, dozens of simulations and their own expertise to create forecasts, apps often pull from a single source and deliver it directly to users.

“Everything that catches attention is mostly nonsense,” said Eric Fisher, chief meteorologist for WBZ-TV in Boston. He points to the viral snowfall maps that spread on social media, noting that extreme forecasts generate the most attention even when they may not be the most accurate.

Anyway, I tried to poke around and find out in dollar terms how much it benefits the weather apps to exaggerate storm dangers. I was unsuccessful there, but by playing with query wording, I was able to coax out of ChatGPT some numbers on how much these exaggerations may cost the rest of us. (A more qualitative search noted, among other things, that the “crying wolf” cost of people becoming jaded to alarmist forecasts may lead them to discount genuine warnings; and I will add it is inconvenient to find the stores out of things because of panic buying).

Everything below the line of asterisks is just a straight dump from the AI, since I could not readily improve on its presentation:

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The economic impact of weather apps exaggerating storm dangers isn’t easy to quantify precisely because most formal studies focus on weather warnings overall (from official sources like the National Weather Service), not specifically on private weather apps exaggerating risk. However, economics research on false alarms, over-warning, and exaggerated alerts offers concrete dollar figures that we can use to approximate the kinds of costs involved. Below are the key economic impacts with as many dollar figures as available:

💸 1. Opportunity Costs of False or Overbroad Storm Warnings

Time cost of false warnings:

Traditional county-wide tornado warnings (over-broad alerts) have historically forced many people to shelter unnecessarily, resulting in ≈234 million person-hours per year spent under warnings (1996–2004) — valued at roughly $2.7 billion annually in lost time/productivity in the U.S. alone.
Improved storm-based warnings that reduce false warning area could save up to $750 million–$1.9 billion/year in avoided lost time.

This demonstrates that exaggerated alerts — such as those sometimes amplified or presented without clear context by weather apps — can force unnecessary economic shutdowns of work and business activity.

🏙️ 2. Evacuation and Preparedness Costs from False Alarms

Evacuation costs:

A NOAA analysis estimates that evacuation costs for hurricanes, when done broadly for storm-warnings, can reach about $1.4 billion annually in the U.S., with roughly $1 billion being unnecessary cost due to false alarms (people evacuating where no damaging winds ultimately occur).

Evacuations are one of the most expensive forms of response — from lost wages to lodging and fuel costs — and exaggerated risk messaging can trigger them prematurely or more frequently.

🏭 3. Business Productivity Losses from False Alerts

Example from private sector:

A building management firm reported that by reducing false storm alerts (better targeted data rather than broad app warnings), it saved ~$10 million in lost production time in one year alone.

This is a real dollar example showing how over-warning impacts businesses directly when they preemptively shut down operations.

💼 4. Broader Firm-Level Economic Avoidance

Cost avoidance from better warning info:

A study of nearly 500 firms found that switching from deterministic (binary “storm/no storm”) warnings to probabilistic information could produce $2.3 billion to $7.6 billion per year in avoided costs for businesses, thanks to better decision-making.

While this doesn’t measure exaggeration specifically, it underscores the economic penalty of imprecise or alarmist warnings, which many weather apps unintentionally echo.

📉 5. Insurance & Property Impacts

Although not directly about exaggerated warnings, inaccurate perceptions of storm danger influence behavior and pricing:

Insurance premiums rising: In storm-prone states (e.g., Florida), homeowners’ premiums have risen by ~57% or more because of risk perceptions and loss patterns tied to severe weather.
Losses from storms: Billion-dollar disasters are common; for example, Hurricane Milton in 2024 had estimated total costs of $21–$85 billion (depending on source).

While not directly caused by exaggeration, inflated perceptions of danger (from apps or media) can affect insurance markets and property values, contributing indirectly to economic impacts.

📈 6. Net Benefits vs. Exaggeration

It’s important to balance:

Early warnings overall clearly save money and lives. Improved forecasts and early alert systems have been estimated to avoid $300 million to $2 billion per year in asset losses and provide up to $32 billion/year in broader economic benefits (through optimized activities and prep).

This highlights that the problem isn’t warnings per se, but inaccurate or exaggerated warnings that cause over-response.