Productivity

How to Install Drywall

Scott 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.

What is an AI Skill?

Zachary BartschLeave a comment

If you’ve been on LinkedIn recently, then you may have seen the chatter about teaching your artificial intelligence to have various skills. I saw one post by a guy who claimed to have created several skills, each representing a tech billionaire.

At first, I thought “I am behind the 8-ball. What is this new thing?”. Obviously I know what the word “skill” is and how people use it, but I had not encountered its use in the context of AI having it. What does it mean for an AI to have a skill? I somewhat dreaded the the work of learning the new skill of teaching my AI skills.

Then I had lunch with a computer scientist and I learned that skills are nothing new.

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Regulatory Burden By Presidential Administration

Zachary Bartsch1 Comment

During president Trump’s first term in office, he made a bunch of waves (as he’s wont to do). His more educated supporters said that he engaged in substantial deregulation of telecommunications, which got a lot of press. There was a quiet contingent of educated voters who were relatively silently supportive on Trump’s regulatory policy, even if his character was indefensible or his other policy was less desirable.

But was Trump a great deregulator? Or was it one of those cases when we say that he regulated *less* than his fellow executives? The George Washington University Regulatory Studies Center can help shed some light with their data. Specifically, they have calculated the number of ‘economically significant’ regulations passed during each month of each president going back through Ronald Reagan’s term. What counts as ‘economically significant’? The definition has changed over time. But, generally, ‘economically significant’ regulations:

  1. “Have an annual [adverse] effect on the economy of $100 million or more
  2. Or, adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, or tribal governments or communities.”

The only exception to this is between April 6, 2023 and January 20, 2025 when the threshold was raised to $200 million.

The Data

The graph below-left shows the number of economically significant regulations for each president since the start of his term, through July of 2025. It’s reproduced from the link above except that I appended Trump’s second term onto his first term. What does the graph tell us? There doesn’t seem to be much of a difference between republicans and democrats. Rather, it seems that, generally, the number of economically significant regulations increases over time. Importantly, the below lines are cumulative by president. So each year’s regulations each cost $100m annually and that’s on top of the existing ones already in place. So, regulatory costs generally rise, with the caveat that we don’t see the relief provided by small or rescinded regulations (for that matter, we don’t see small regulatory burdens here either). Something else that the below graph tells us is that presidents tend to accelerate their economically significant regulations prior to leaving office. Reagan was the only exception to this pattern and he *slowed* the number of regulations as the end of his term approached.

Below-right is the same data, but the x-axis is months until leaving office. Every president since Bush-41 has accelerated their burdensome regulations during their final months in office. The timing of the acceleration corresponds to how close the preceding election was and whether the incumbent president lost. Whereas all presidents regulate more in their last 2-3 months in office, the presidents who were less likely to win re-election started regulating more starting around eight months prior to leaving office. Of course, they wouldn’t say that they expected to lose, but they sure regulated like there was no tomorrow.

What about Trump? Trump’s fewer regulations is caused by his single term. He definitely still added to the regulatory burden (among economically significant regulations, anyway). While Trump started with the fewest additional regulations since Reagan, and Biden started with the most ever initial regulations, together they earn the top prizes for most regulations added in their first term.

What if we append these regulations from end-to-end? That’s what the below chart does. We do have to be careful because the series is a measure of gross economically significant regulations and not net economically significant regulations. So, it’s possible that some rescissions dampened the below values, but this is the data that I have for the moment. While each presidential administrations increases regulation more than the prior, the good news is that the rate of change is not exponential. The line of best fit is quadratic. We’re experiencing growing regulations, but at least it’s not compound growth.

The Cost

We can estimate the costs of these economically significant regulations. It’s a rough cut, and definitely a lower bound since rescission is rare and $100 million is itself a lower bound, but we can multiply the number of regulations by $100m to get minimum annual cost. Like I said, the Biden criterion from April 2023 through January 20, 2025 changed, so those regulations get counted as $200 million instead. The change in definition means that the regulation counts underestimate the late-term Biden regulations relative to the other presidencies.

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

Scott 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.

Truth: The Strength and Weakness of AI Coding

Zachary Bartsch1 Comment

There was a seismic shift in the AI world recently. In case you didn’t know, a Claude Code update was released just before the Christmas break. It could code awesomely and had a bigger context window, which is sort of like memory and attention span. Scott Cunningham wrote a series of posts demonstrating the power of Claude Code in ways that made economists take notice. Then, ChatGPT Codex was updated and released in January as if to say ‘we are still on the frontier’. The battle between Claude Code and Codex is active as we speak.

The differentiation is becoming clearer, depending on who you talk to. Claude Code feels architectural. It designs a project or system and thrives when you hand it the blueprint and say “Design this properly.” It’s your amazingly productive partner. Codex feels like it’s for the specialist. You tell it exactly what you want. No fluff. No ornamental abstraction unless you request it.

Codex flourishes with prompts like “Refactor this function to eliminate recursion”, or “ Take this response data and apply the Bayesian Dawid-Skene method”. It does exactly that. It assumes competence on your part and does not attempt to decorate the output. It assumes that you know what you’re doing. It’s like your RA that can do amazing things if you tell it what task you want completed. Having said all of this, I’ve heard the inverse evaluations too. It probably matters a lot what the programmer brings to the table.

Both Claude Code and Codex are remarkably adept at catching code and syntax errors. That is not mysterious. Code is valid or invalid. The AI writes something, and the environment immediately reveals whether it conforms to the rules. Truth is embedded in the logical structure. When a single error appears, correction is often trivial.

When multiple errors appear, the problem becomes combinatorial. Fix A? Fix B? Change the type? Modify the loop? There are potentially infinite branching possibilities. Even then, the space is constrained. The code must run, or time out. That constraint disciplines the search. The reason these models code so well is that the code itself is the truth. So long as the logic isn’t violated, the axioms lead to the result. The AI anchors on the code to be internally consistent. The model can triangulate because the target is stable and verifiable.

AI struggles when the anchor disappears

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Against Eugenics, on its Own Terms

Zachary BartschLeave a comment

Once upon a time, eugenics was all the rage. It was nascent during the reconstruction era and persisted into the 20th century. It grew out of biological evolutionary theory and emphasized reproductive fitness. In brief, the theory asserted that there are differences in individual fitness and that the more fit living things will survive better and reproduce, eventually becoming a greater part of the population. The ability to compile and evaluate statistics about various human measurements made inferences hard to resist. Of course, researchers were plagued by small sample size, omitted variable bias, and social biases of the day (for example, phrenology inferred fitness characteristics from skull shape).

People employing eugenic thinking, overwhelmingly, supported theories that their own type of person was among the more fit. Eugenicists didn’t promote theories of their own un-fitness. In the progressive era of the early 20th century, eugenics met the prevailing attitude that government could be employed to resolve social and economic ills. This era is when the income tax emerged, prohibition was enacted, the Federal Reserve was formed, and various labor regulations were enacted.

The result was that policy sometimes pursued greater ‘fitness’ among its populations. Rather than systematically encouraging the supposedly more fit with economic incentives, most policy was geared toward reducing the reproductive success of supposedly less fit people. These included forced sterilization, institutionalization, and economic exclusion. Besides rejecting basics individual human dignity, the harm was all the more tragic given that fitness was often poorly specified. That is, policy criteria weren’t dependably related to fitness. Fatal conceit, indeed!

One of my favorite ways to argue is to grant premises and then change details on the margin to see whether the conclusion changes. Let’s do that. Let’s grant that there are innate differences between people that are related to biological success. Since survivability is related to resource acquisition, let’s grant also that economic success overlaps at least somewhat.  Taking that as granted, does pursuit of the historical eugenic policy still follow?

It does not.

There are two mistakes that eugenicists and various sorts of racists and xenophobes made. They assert or imply 1) that fitness characteristics are stable and systematically identifiable, and 2) that policy needed to intentionally select for the fitness characteristics.

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Which Economies Grow with Shrinking Populations?

Zachary Bartsch4 Comments

If you didn’t know, China has had negative population growth for the past 4 years. Japan has had negative population growth for the past 15 years. The public and economists both have some decent intuition that a falling population makes falling total output more likely. Economists, however, maintain that income per capita is not so certain to fall. After all, both the numerator and denominator of GDP per capita can fall such that the net effect on the entire ratio is a wash or even increase. In fact, aggregate real output can still continue to grow *if* labor productivity rises faster than the rate of employment decline.

But this is a big if. After all, some of the thrust of endogenous growth theory emphasizes that population growth corresponds to more human brains, which results in more innovation when those brains engage with economic problems. Therefore, in the long run, smaller populations innovate more slowly than larger populations. Furthermore, given that information can cross borders relatively easily no one on the globe is insulated from the effects of lower global population. Because information crosses borders relatively well, the brains-to-riches model doesn’t tell us who will innovate more or experience greater productivity growth.

What follows is not the only answer. There are certainly multiple. For example, recent Nobel Prize winner Joel Mokyr says that both basic science *and* knowledge about applications must grow together. That’s not the route that I’ll elaborate.

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How to Make a Few Billion Dollars

James Bailey1 Comment

The title is excellent, given that the author Brad Jacobs did in fact make a few billion dollars.

The book itself is fine to read, but also fine to skip if you aren’t yourself burning to build a billion dollar company through excellent management and mergers and acquisitions. I certainly don’t care to, which Jacobs says would make me a bad hire for one of his companies:

I only hire people who are motivated to make a lot of money…. If an candidate says to me ‘I’m not motivated by money’, I suspect either they’re not being candid or they lack the hunger that’s necessary to succeed

The book has plenty of hard-driving sentiments like this that you’d expect from a self-made billionaire:

Fire C players

For the first time ever, an American company, Exxon, had reported quarterly earnings in excess of $1 billion. The words “obscene profits” flashed on my TV screen, and I remember thinking “That sounds pretty good! Maybe I ought to check out the oil sector.” [This part I agree with, economic theory predicts that entrepreneurs will enter the sectors with the highest profits and its what I’d do if I wanted to make money, though in practice I think it is surprisingly rare for would-be entrepreneurs to choose this way -JB]

“The CEO trait most closely correlated with organizational success is high IQ” [specifically more important than EQ]

But Jacobs balances these ideas with some surprisingly hippy-like attitudes. Jacobs went to Bennington College and almost had a career as a jazz keyboardist. Chapter 1 is titled “How to Rearrange Your Brain”, and emphasizes the importance of meditation. Page 21 is basically “have you ever really looked at your hands, man… do it, it’s a trip”

I don’t want to spend even one hour around people who are unkind. An organization is like a party. You only want to invite people who bring the vibe up

Though perhaps this hippy/anti-hippy balance shouldn’t be surprising for someone who says one of the main things he asks about potential hires is “can this person think dialectically”.

Strongly recommend the book if you want to follow Jacobs’ path; weakly recommend it as a general management/self-help book or way to learn about markets.

Rising Chinese Zombie Firms

Zachary BartschLeave a comment

Have you ever looked up and wondered where the time went? One moment you’re living your life, and the next moment you realize that you’ve just lost time that you’ll never get back? That’s what happened to Japan’s economy at the turn of the century in an episode that’s known as ‘the lost decades’. It was a period of slow or null economic growth. Economists differ with their explanations. One cause was the prevalence of ‘zombie firms’.

Japan’s Economy

Japan had a current account surplus from 1980-2020, which means that they had more savings than they effectively utilized domestically. Metaphorically, they were so full of savings that they exhausted productive domestic investment opportunities and their savings spilled out into other counties in the form of foreign investments. This was driven by high household savings and slow growth in domestic investment demand. The result was the Japanese firms had easy access to credit. Maybe a little too easy…

Private corporate debt ballooned throughout the 1980s. That’s not intrinsically a problem. In the 1990s, households began saving somewhat less, and most firms began to drastically deleverage… But not all firms. The net effect of the mass deleveraging was that interest rates fell.  The firms that remained in debt were the ones that risked insolvency. Less productive firms had slim profits and their Earnings Before Interest, Taxes, Depreciation, and amortization (EBITDA) was slim. So slim, that they couldn’t pay their debts. Faced with the prospect of insolvency, firms did what was sensible. They refinanced at the lower interest rates. Firms went to their banks and to bond markets and rolled over their debt, which they couldn’t afford, and replaced it with debt that had a lower interest rate. This occurred across industries, but especially in non-tradable goods and services that were insulated from international competition. Crisis averted.

Except this process of refinancing, while avoiding acute defaults and a potential financial crises, ensured that the less productive firms would survive. Not exactly failing and not exactly thriving, they could sort of just hold on to something that looks like life. Well, high debt and low profits aren’t much of a life for a firm. It’s more like being undead – like a zombie. Between 1991 and 1996, the share of non-finance firm assets held by zombie firms ballooned from 3% to 16%. The run-up differed by industry: Manufacturing zombie assets rose from 2% to 12%, from 5% to 33% in real estate, and from 11% to 39% in services.  These zombie firms linger on, tying up valuable resources with low-productivity activities and drag on the economy.

China’s Economy

I’m not prone to China hysteria generally. However, I do have uncertainty about the plans and actions of the Chinese government because I don’t know that domestic economic welfare is its priority. That makes forecasting more political and less economic and outside my expertise. Regardless, the Chinese economy is a constraint on the government, whether they like it or not.  And there are some echoes of the Japanese economy’s lost decades.

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Tariffs Are Not Smart Industrial Policy

Zachary Bartsch2 Comments

Economists overwhelmingly see tariffs as clearly welfare-reducing. Tariffs on imports result in higher prices, fewer imports, less consumption, and more domestic production. In fact, it is the higher prices that solicit and make profitable the greater domestic production. We don’t get the greater domestic output at the pre-tariff price. We can show graphically that domestic welfare is harmed with either export or import tariffs. The basic economics are very clear.

However, the standard model of international trade makes a huge assumption: Peace. That is, the model assumes that there are secure property rights and no threats of violence. All transactions are consensual. This is where the political scientists, who often don’t understand the model in the first place, say ‘Ah ha!. Silly economists…’ They proceed to argue for tariffs on the grounds of national security and the need for emergency manufacturing capacity. But is an intellectual mistake.  

Just as economists have a good idea for how to increase welfare with exchange, we also have good ideas about how to achieve greater or fewer quantities transacted in particular markets. This is not a case of economists knowing the ideal answer that happens to be politically impossible.  Rather, if it pleases politicians, economists can provide a whole menu of methods to increase US manufacturing, vaccine manufacturing, weapons manufacturing… Heck, we can identify multiple ways to achieve more of just about any good or service. Let the politicians choose from the menu of alternatives.

The problem with tariffs is that they reduce consumer welfare a lot, given some amount of increased production in the protected industry. Importantly, this assumes that the tariffs aren’t hitting inputs to those industries and are only being applied to direct foreign competitors. The below argument is even stronger against imperfectly applied tariffs, like the US tariffs of 2025.

What’s the alternative?

The alternative is a more focused tack. If the government wants more missile or ship production, then what should it do? There’s plenty, but here’s a short list of more effective and less harmful alternatives to tariffs:

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