Scott Buchanan

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

April 21, 2026April 21, 2026Scott BuchananLeave a 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

April 14, 2026April 14, 2026Scott BuchananLeave a comment

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

April 7, 2026April 7, 2026Scott BuchananLeave a comment

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

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, 2026April 19, 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.