Real human technology is, of course, not generated by randomly combining components together and seeing if they do anything useful; the randomness in these simulations is just a way to see how easy or hard it is to create new technologies under different conditions. But biological technology — the huge panoply of lifeforms that exist on earth, from microscopic single-celled organisms to whales the size of a 737 — is also generated by randomness. Evolution builds biological technology bit by bit by harvesting the fruits of genetic variation, often caused by random mutation, preferentially selecting the most fit organisms to propagate their genes into the future. Over billions of years, this process can generate astoundingly complex biological systems.

What’s interesting is that biological evolution uses a very similar trick to Arthur’s circuit simulation. By leveraging modularity at the genetic level, populations of organisms can increase the rate that useful genetic variants spread through the population, effectively increasing their rate of information acquisition. Sexual reproduction, along with other ways of sharing genetic material like horizontal gene transfer, is essentially a mechanism for doing this. We can show this with some simple simulations.

Evolution and reproductive strategies

The simplest way for an organism to reproduce is asexual reproduction, where a parent produces a child that’s a genetic copy of itself. Simple single-celled organisms, for instance, reproduce by cellular fission, dividing into two or more “children” that each have the same genes as the original parent.

But children won’t necessarily be identical copies of their parents. Due to genetic mutation, some genes might get randomly altered during the fission process, producing children with slightly different genes. In some cases, these mutations might be useful, giving additional functionality such as antibiotic resistance and thus better odds of surviving and reproducing. Because of their contribution to the organism’s fitness, over time the useful mutations will become more and more common in the population.

We can demonstrate this with a simple simulation. In our simulation, we start with a population of 100 creatures, each of which has a genome of 200 individual genes. A gene can either be a 1 (the “good” version of the gene) or a 0 (the “bad” version of the gene). The initial population is random, with each creature having roughly a 50-50 mix of good and bad genes. Each iteration of the simulation, each creature produces two children. A child copies the genes of its parent, but due to mutation each gene has a 0.2% chance of being flipped, going from a 1 to a 0 or vice versa. The 100 most fit children (where fitness is just the sum of each gene value, since 1 is the “good” version of the gene in our simplified model) are selected to continue the next generation, and the cycle repeats. This is a simplification compared to how evolution actually functions — for one, it treats genes as contributing to fitness independently, ignoring the fact that the fitness value of one gene often depend on other genes — but it’s enough to show some of the dynamics at work.

When we run this simulation, the proportion of “good” genes in the population steadily rises over time as more-fit offspring outcompete less-fit offspring. Depending on the mutation rate, the population may eventually reach maximum possible fitness of 200, or plateau at some level below it.

The problem with this strategy — producing children that are noisy copies of a single parent, and relying purely on random mutation as a source of genetic variation — is that once you’re at above-average fitness, mutations are likely to be bad on average. If a genome has more 1s than 0s, a random change will be more likely to change a 1 to a 0 than a 0 to a 1. Thus for parents of above-average fitness, their children will on average have lower fitness.

Because mutation is random, there will nonetheless be variation, and some children will end up with higher fitness than their parents. And because selection eliminates the least fit each iteration, the pool of selected children will have higher average fitness than their parents, allowing average fitness to increase over time. But mutation reducing average fitness drags down this process.

You can see this in the graph below, which shows a simulation with slightly different parameters (a genome length of 1000 and a mutation rate of 2%) to more easily see the trends. The top graph shows the distribution of population fitness at generation 50, and the second graph shows the distribution of the population’s children prior to selection. You can see that, thanks to mutation, average fitness has dropped, though due to randomness some proportion of the children have lucked into getting higher fitness. The last graph shows the children after the top half of the distribution has been selected. Average fitness rises, and is now above the initial population, though just barely.

Now let’s look at a simulation of a different reproductive strategy: sexual reproduction, where children get their genes from two parents rather than just one. In this simulation, we still have a population of 100 creatures with genomes of 200 genes, each of which can either be a 0 or a 1. But now children have two parents, and in each iteration members of the population are paired up randomly and each pair produces four children. Children get their genes from both parents with each gene having a 50% chance of coming from a particular parent. The top 100 most fit children are then selected for the next generation, and the iteration continues. In this simulation, there is no mutation, so genetic variation entirely comes from reshuffling the genes of the parents.

Like the previous simulation, the population gradually reaches maximum fitness. But sexual reproduction gets there much faster. With asexual reproduction, after 200 generations the population was around an average fitness of 187. With sexual reproduction, the population average reached the fitness maximum of 200 in just 33 generations.

The key is that sexual reproduction introduces genetic variation without reducing average fitness. Since children are a random combination of their parents’ genes, on average they’ll have the same fitness as their parents (with some randomly having higher fitness, and others randomly having lower fitness). When the most-fit children are selected for the next generation, this is taking the top half of a distribution with a much higher average than the distribution of children in the asexual simulation. Average fitness thus rises much faster.

If you work out the math (or, as I did, simply read the math that someone else worked out), in an asexual population the rate of fitness increase is 1/(8*f), where f is the differential normalized fitness. (The normalized fitness of a population is the average fraction of good genes in that population; so a population where, on average, a member has 150 good genes in a genome of 200 would have a normalized fitness of 0.75. Differential normalized fitness is the normalized fitness of the population minus 0.5, the normalized fitness of a randomly generated population.) Early on, fitness of a population can increase quickly, but the rate soon drops below an increase of 1 unit of fitness per generation (one gene flipped from a 0 to a 1 on average). As a population gets closer to maximum possible fitness, the rate of fitness increase approaches 0.25 (flipping one gene, on average, from a 0 to a 1 every four generations).

With sexual reproduction, on the other hand, the rate of fitness increase turns out to be much higher: it’s proportional to the square root of the length of the genome.

The informational power of genetic recombination

One way to think about why sexual reproduction is so powerful is to look at lineages of descent. Say that one of the members of our asexually reproducing population stumbles across a new, useful mutation. Because genes are passed from one parent to one child, the only way this gene can spread throughout the population (in the absence of some other member of the population also stumbling across it) is if the children of whoever has it outcompete the children of everyone else. In this scenario, the population eventually ends up consisting entirely of descendants of one particular member of the population — as a necessary condition of this spread, every other genetic lineage (along with whatever useful mutations they might have stumbled across) gets wiped out.

We can see this in our simulation results. The chart below assigns each member of the initial population, and their children, a unique color. The simulation starts out with 100 different colors (one for each member of the population), but this quickly gets winnowed down to a much smaller number. After a few generations, the population is one uniform color, all descendents of one particular member of the initial population. (This chart is from one particular simulation run, but repeated runs will show the same behavior.)

If we redo the color coding whenever the population reaches the point where everyone is descended from a single ancestor, we see that this happens repeatedly. In the chart below, the population at generation 48 are all descendants of one particular member of the population who lived in generation 25. At generation 80, they’re all descendants of one particular member alive from generation 48.

In evolutionary biology, this phenomenon is known as “clonal interference”: if two different beneficial mutations arise in different members of the population of the same generation they can’t be shared and so they end up competing against each other, and one beneficial mutation ultimately gets wiped out.

With a sexually reproducing population, on the other hand, useful mutations can be shared much more easily. In an asexual population, a member has one parent, one grandparent, one great-grandparent, and so on. But in a sexual population, a member has two parents, four grandparents, eight great-grandparents, etc. Beneficial variation from earlier generations can spread much more easily.

We can see this in the graph below, which shows the proportion of genes of the original members of a sexually reproducing population represented in the gene pool at any given time. We can see that the proportion stays very high: genes from almost 75% of the original population are found in the population after 34 generations. In the asexual population, this was 1% (and would be even lower in larger populations, since it’s just 1/total starting population).

We previously noted that Brian Arthur’s circuit simulation took advantage of modularity, finding useful subcomponents, locking in their designs, and then using those to build more complex technologies. Once the simulation finds a 3-way AND gate, it can use that to make a 4-way AND gate, which it can use to make a 5-way AND gate. We likewise noted that if you’re trying to build an 8-bit adder by randomly combining NAND gates together, it’s vastly easier if you can add one NAND gate at a time and verify you’re correct than if you have to guess all 68 gates at once.

You can think of this like someone solving a combination lock. A lock with a five-digit combination, with 100 possible values for each digit, has 100^5 = 10 billion possible combinations. Trying combinations one by one would take forever.

Technological modularity is like being a skilled lockpick that can check whether each individual digit attempted on a combination is correct (maybe by listening carefully you can hear a telltale “click” when the dial is in the right spot). Now instead of searching over 10 billion possible combinations, you’re doing five searches over 100 possible values each, or 500 possibilities total. The space of possible options that must be considered is vastly reduced.

Sexual reproduction is, as I understand it, doing something similar: by letting genes from two parents be combined to form children, it effectively lets the fitness of each gene be tested independently, turning the search from something like “find the best 200-gene genome” to something closer to 200 parallel “find the best gene at this location” searches. In our combination lock analogy, the modular circuit simulation is sort of like turning the dial until you hear a “click,” which indicates that the given number is correct. Sexual reproduction is more like trying a bunch of different random combinations, getting back a score for “how close this combination is to being solved,” and using that to infer which “dials” are correct. The search space is correspondingly greatly narrowed, and the search proceeds much faster.

With Arthur’s technological search, we couched this reduction in terms of information theory, calculating the bits of information gleaned per iteration. (As a reminder, a “bit” is just “something that cuts the number of possibilities to consider in half.”) In our 8-bit adder, 68 NAND gate search, finding the working arrangement required narrowing down 2^853 possibilities, or 853 bits. Trying to get all 68 gates right at once got us less than 0.000001 bits per attempt, requiring a long and painful search. But with modularity — going gate-by-gate and knowing whether each gate is in the correct location — we accumulate information much faster, around 0.003 bits per attempt (3,000x faster than nonmodular search).

We can similarly look at biological evolution and the spread of useful genetic variants in terms of information acquisition. For our simulations, the information we have at a given time is a function of how “certain” the population is about the value of each gene. With our starting, randomized population for each gene approximately 50% of the population has a 1 and 50% has a 0, we’re maximally uncertain, and we have 0 bits of information for each gene. When the population has reached maximum fitness (every member having a 1 for every gene) we’re maximally certain, and we have 1 bit of information for each gene. Total information is thus roughly equal to 2 * (F - G/2), where F is fitness and G is genome length.

We can see that information is acquired much more quickly in our sexual reproduction simulation than in our asexual simulation.

There’s an important caveat to all the above analysis: it assumes that genes independently contribute to fitness: that is, that the usefulness of gene 27 isn’t a function of gene 145. When genes are coupled like this, and the usefulness of one genetic variant is a function of what other genetic variants you possess (as they often are in practice), the evolutionary search process gets much more complex to analyze. (Stuart Kauffman has done a lot of work here with his concept of NK landscapes, which you can read about here and here.) But the simple, additive fitness case is still useful for understanding these evolutionary mechanics.

It’s also important to know that in real life, asexually reproducing organisms like bacteria have ways of sharing genes between them that get them some of the benefits of sexual reproduction. Bacteria widely engage in what’s known as horizontal gene transfer, which is exactly what it sounds like: genes being transferred between existing members of a population. This is apparently how genes for antibiotic resistance mostly spread, and some analyses suggest that 20-80% of bacterial genomes are the result of this sort of gene transfer.

Conclusion

The technological search process benefits greatly from modularity: being able to break a technology down into subcomponents with specific functionality, and determining whether those subcomponents are functioning properly. In information theoretic terms, this greatly narrows the possibilities that must be considered in a search process. It’s interesting to see that biological evolution, which creates and operates in an entirely separate domain, uses a similar sort of trick: using genetic recombination (in the form of sexual reproduction and horizontal gene transfer) to make the search process more modular and gain information more rapidly.

(For more about these ideas about evolution and information acquisition, including a much more rigorous mathematical treatment, see chapter 19 in David Mackay’s book “Information Theory, Inference, and Learning Algorithms.”)

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure and industrial technology. This week we look at chatbots replacing realtors, Chinese synthetic diamonds, Australian batteries, Meta’s data center tents, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Iran war

Iran breaks off negotiations with the US and vows to “completely block” the Strait of Hormuz. [CNBC]

Analysis of satellite data by the BBC suggests the damage Iran has inflicted on US military facilities is more extensive than has been previously reported. [BBC]

Housing

A NYT reporter successfully uses an AI chatbot instead of a realtor to sell their house. “A flurry of bookings to view our house over the coming weekend arrived in my inbox within hours. I struggled to manage the appointments until, again, I just let the chatbot do everything for me. I told agents that they had to email or text — no phone calls. Whatever they wrote, I copied and pasted into the chatbot; whatever it replied, I copied and pasted right back to the agents. I was worried that pushy ones would prey on my inexperience, so I had the chatbot come up with a list of potential conflicts and write confident responses I could have ready.” [NYT]

Opposition to property taxes is having a political moment, which as we’ve mentioned previously is a pretty bad idea. Now Florida really seems like it might be on the verge of effectively eliminating homeowner property taxes. “Gov. Ron DeSantis’ property tax plan for the November ballot would raise the homestead exemption to $250,000 and require the Legislature to enact a plan to eliminate property taxes entirely for the vast majority of Floridians who own the homes they live in, he announced Wednesday DeSantis said he was calling the Legislature back to Tallahassee on Monday to add an amendment to the ballot that would eventually eliminate property taxes for 92% of those Floridians by raising the homestead exemption to $500,000.” [Governing]

The urban benefits of allowing tall buildings. “Land-use regulations, including height limits, affect housing affordability and urban productivity. This column analyses over 11,000 urban agglomerations and 300,000 tall buildings to explore the effect of height restrictions on welfare. Vertical growth enhances land efficiency, reduces commuting, and boosts worker welfare. While higher density can increase housing demand and rents, the associated gains more than offset the costs.” [VoxEU]

It’s apparently easier to get planning permission to build a skyscraper in London (a city which has notoriously made it almost impossible to build new housing) if you include a publicly accessible roof deck, and thus quite a few London skyscrapers have them. [Diamond Geezer]

A census map of where air conditioning is uncommon in the US. [X]

Manufacturing

As AI gets more capable, the risk that someone uses it to design an engineered virus or pathogen rises. To help mitigate this risk, IFP co-led an open letter calling for mandatory record-keeping of who has purchased synthetic nucleic acids and the equipment for making them. “As life sciences researchers, builders of AI and biotechnology, and experts with a wide range of views on how to approach AI policy, we call on legislators to make screening of orders for synthetic nucleic acids — and the equipment needed to make them — mandatory.” [ScreenDNA.org]

One surprising beneficiary of the AI boom: Chinese manufacturers of synthetic diamonds, which are used as part of cooling systems for semiconductors. “Traditionally associated with jewelry, these synthetic gems are now being adopted as chip‑cooling materials, enabling denser and more powerful AI semiconductors. Momentum has accelerated after several Chinese producers reported that clients validated their diamonds as effective heat spreaders and began commercial shipments.” [Bloomberg]

California passes a bill requiring 3D printers to include controls to block the production of “ghost guns.” [The Register]

Elon Musk’s “Terafab” semiconductor fab gets a big tax break from a Texas county. [KBTX]

Read more

Many folks, including me, have observed that it seems to take much longer to build infrastructure in the US than it used to. People point to things like the rapid construction of the Empire State Building (one year) or the Golden Gate Bridge (just over four years) and note that for a modern infrastructure project it can take that long or longer to even get the permits or do the environmental studies.

But when doing these sorts of comparisons, it’s important to compare like with like: specifically, we shouldn’t measure the time spent planning a project (which would include doing the environmental studies and securing the permits) against the time spent actually building it. (The Golden Gate Bridge, for instance, was constructed in four years from 1933 to 1937, but planning for the project began around 1921.)

I wanted to get a sense of how planning times for major infrastructure projects in the US have evolved over time. To do this, I looked at planning and construction times for 67 major bridges built in the US since roughly the beginning of the 20th century. For each bridge, I noted the year that planning began, the year that construction began, and the year that it opened for service.1

As usual with an exercise like this, the results you get will be a function of the definitions you choose. “Started construction” and “opened for service” are relatively unambiguous — but what specifically do we mean by “planning begins”? People will often float the idea of a bridge for years before anything resembling formal plans is in place; the idea for the Brooklyn Bridge, completed in 1883, apparently dates back to 1804, but it doesn’t seem reasonable to consider that early discussion the beginning of planning. On the other hand, if you choose something like “the government formally announces the project” as your criterion, that might exclude years of serious efforts to get a project constructed.

I ended up choosing the “planning begins” date as the point when some organization connected with transit planning in some official capacity first announced or proposed the project. So for the Golden Gate Bridge, this would be 1921, when Chicago engineer Joseph Strauss and San Francisco city engineer Michael O’Shaughnessy prepared a joint proposal for the bridge. However, pinning these dates down was often difficult. Most bridges aren’t the Golden Gate Bridge, with lots of publicly available sources chronicling their design and construction. Often sources give a brief “planning begins” date without explaining what that meant specifically or what activities preceded it. And for some bridges, such as the New Tappan Zee Bridge, the decision to proceed with a bridge was preceded by years of studies on possible ways to expand transit capacity that considered many non-bridge options. For these, I tried to use the initiation of broader studies as the start of planning, but I suspect that these sorts of preliminary studies weren’t always documented. Overall I did my best to determine the dates, but I expect there to be errors and inconsistencies, and the results below should be taken with a grain of salt.

Those caveats out of the way, let’s look at the data. The graph below shows planning times for each bridge on the list. The horizontal axis is the year when planning began for a particular bridge, and the vertical axis is total years of planning.

And the graph below shows construction times for each bridge.

Because there’s a lot of variation in both planning times and construction times, it’s useful to look at trends by age bracket. The chart below shows average planning times and construction times for 20-year age buckets, with pre-1900 and post-2000 bridges each given their own bucket.

There are a few trends visible here. One is that bridge construction times fell from 1900 to the 1960s, but since then have risen. The average time to construct a bridge between 1980 and 1999 (6.5 years) was more than twice as long as the average construction time for bridges built between 1940 and 1959 (3.1 years). Construction times for bridges built between 2000 and 2025 are down from this peak (5.4 years), but they’re still well above the times of the 1920s through the 1950s.

Planning timelines, on the other hand, show a somewhat different trend. At a high level, we do see something that looks like the “times fall, then rise again” pattern we saw with construction times: planning times fell in half from over 12 years between 1900 and 1919 to 6 years between 1960 and 1979, before rising again in the 1980s. But the 1980-1999 period planning times look fairly similar to every other period other than 1960-1979. It’s not clear that modern planning timelines, which are on the order of 10 years on average, are all that different from most historic ones.

More recently, post-2000 planning times look even shorter (6.2 years on average), but this data is almost certainly biased downward because the bridges with long planning times haven’t been completed yet. A bridge that started planning in 2007 but has a 20-year planning timeline won’t start construction until 2027, and thus won’t show up in the data. A similar thing is probably taking place for pre-1900 bridges, which have planning times which are biased upward (since I was mostly picking bridges that were built after 1900). So we should be careful about drawing conclusions about planning times for these age brackets.

One notable thing about recent bridge construction is that a very large fraction of modern bridges are replacements for existing bridges. Of the 17 bridges on the list that were completed after the year 2000, 14 of them were replacement bridges. Some of these were for bridges that collapsed, like the I-35W bridge replacement in Minneapolis; others were for bridges that were damaged and at risk of collapse, like the Penobscot Narrows Bridge in Maine; and some were for bridges that were near the end of their design lives, like Florida’s Pensacola Bay Bridge. All else being equal, I’d expect a replacement for an existing bridge to get built more quickly than a completely new bridge, because people will react more viscerally and negatively to a major traffic route being removed than they will for construction of a nonexistent bridge getting delayed. This visible sentiment probably creates a sense of urgency to replace an existing bridge that doesn’t exist for a completely new bridge that people haven’t developed expectations for. One illustration of this dynamic is that collapsed highway overpasses often get rebuilt exceptionally quickly. When a highway overpass in Atlanta collapsed following a fire, it was rebuilt in just six weeks. We saw something similar with collapsed highway overpasses in Los Angeles and Pennsylvania. (The long time its taking to rebuild the collapsed Francis Scott Key Bridge in Baltimore thus may be something of an outlier.)

One other notable observation: there appears to be little relationship between planning times and construction times. Bridges that take a long time to plan might get built exceptionally quickly, and vice versa.2

Which bridges on the list took the longest to plan? Here are the bridges with the five longest planning timelines.

• Bear Mountain Bridge, New York, 54 years — The Bear Mountain Bridge, a suspension bridge in New York, was first planned in 1868, after a bill was passed by the New York legislature. There were a series of attempts to construct the bridge over the next 30 years, but none of them was successful in building more than foundations, and construction on the current bridge didn’t begin until 1922.

• Fremont Bridge, Oregon, 41 years — Planning for the Fremont Bridge in Portland dates back to 1927, when the City Council issued a “Major Traffic Street Report” which proposed a bridge across the Willamette River in the Fremont Bridge’s current location. The bridge was also suggested by the Oregon State Transportation Committee in 1933 and by Robert Moses in his 1943 planning study for the City of Portland. The site for the bridge was finally settled on in 1955, and construction began in 1969.

• Queensboro Bridge, New York, 34 years — The earliest proposals for a bridge between Manhattan and Queens date back to 1804, but the first serious efforts began in 1867, when wealthy Queens residents formed the New York and Long Island Bridge Company to build the bridge. The company hosted a design competition for the bridge in 1876, but work was slow to begin, and in 1890 a state justice ruled the charter invalid. In 1900, the city took over the company’s franchise to build a bridge, and construction began in 1901.

• Claiborne Pell Newport Bridge, Rhode Island, 32 years — Planning for the Claiborne Pell Newport Bridge in Rhode Island began in 1934 but was delayed until after World War II for unclear reasons. In 1948 the state created the Newport-Jamestown Civic Commission to explore ways to finance and construct the bridge, but construction didn’t begin until 1966.

• Verrazzano Narrows Bridge, New York, 31 years — In 1928, the chambers of commerce for several New York boroughs announced that the Interboro Bridge Company had proposed a “Liberty Bridge” that would connect Brooklyn and Staten Island. However, a vote on the proposed bridge in the New York legislature was blocked by congressman and future New York mayor Fiorello La Guardia. This was followed by plans for a tunnel (which were then shelved), then another bridge, then another tunnel, until finally the plans for a bridge were approved in the late 1950s. Construction began in 1959.

Conclusion

Planning timelines for bridge construction in the US have not gotten obviously worse over the past several decades, though I suspect that the fact that most recently constructed bridges are replacing existing bridges is doing much of the heavy lifting here. This supports a vague notion I have that organizational success is often downstream of a culture of urgency — considering a problem important to actually solve and being willing to work hard on solving it. (You get the same sense of urgency when reading about how countries like Japan and Korea were able to build their commercial shipbuilding industries up from virtually nothing.) Perhaps the trick to making US infrastructure construction proceed more quickly is figuring out how to instill this culture of urgency in the agencies responsible for building it rather than relying on imminent infrastructure failure to create it.

House building using the “Oraaflex” modular construction system, via Wikipedia.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure and industrial technology. This week we look at a California chemical leak, weapons-grade plutonium for nuclear reactor startups, a startup that will clean your house to get robot training data, Blue Origin’s rocket explosion, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housekeeping items:

• I spoke at the Reagan National Economic Forum about energy and AI infrastructure. You can watch my panel here.

War in Iran

Iran ends 88-day long internet blackout. [The Hill] And Iran claims that the fees it’s charging to allow ships to pass aren’t tolls but “safety and environmental protection fees.” [MSN]

Housing

West coast cities such as Seattle and Portland are considering a “vacancy tax” to try and deal with the problem of empty commercial office space. Such taxes already exist in Washington DC and San Francisco. [The Urbanist]

Relatedly, developer Asher Luzzatto is trying to revitalize Denver’s downtown, which currently sits at a 40% office vacancy rate. “Luzzatto’s approach is to turn that old formula inside out, transforming desolate urban cores into welcoming places to live. He plans to convert half of the Energy Center plus two other downtown Denver office buildings into about 1,100 apartments. He’s also planning a bookstore, art gallery, children’s museum and daycare center. He acquired all four buildings for pennies on the dollar. In a matter of months, the Los Angeles native has taken control of more than 7% of Denver’s traditional downtown office space.” [WSJ]

What was it like to live in an apartment in ancient Rome? [Common Edge]

Manufacturing

In California, damage to a tank of methyl methacrylate (a substance used to make plastic) put 50,000 people under an evacuation order due to the possible risk of an explosion. Fortunately the problem was resolved without the tank exploding. [BBC] At “In the Pipeline,” chemist Derek Lowe talks about methyl methacrylate and how it could potentially cause an explosion. “There’s a key energetic aspect to these polymerizations: they’re thermodynamically favorable, and these bond formations give off a bit of heat as they occur. This heats up the solution as a whole, and that in turn speeds up the reactions all by itself! Which means even more heat as even more molecules polymerize. . .and now you see why storage of large quantities of these monomer compounds is not for the unwary. You have to keep them away from anything that can start a free-radical chain reaction, and that means light and heat for starters, but also not letting them sit in contact with many metals and alloys, etc.” [Science]

In other “damaged chemical tank” news, one person was killed at a chemical tank explosion at a paper mill in Washington state. [USA Today]

Nvidia plans to spend on the order of $150 billion in Taiwan each year. [Reuters]

Chinese tech firm Huawei claims a major chip design breakthrough. “Huawei said Monday at a tech conference in Shanghai that by 2031, its high-end chips would have transistor density equivalent to 1.4-nanometer processes, which is considered the industry’s cutting edge.” [MSNBC] And Chinese battery manufacturer Calb is building a battery factory in Portugal that will produce on the order of 4% of the country’s GDP. [X]

A report on the state of US missile manufacturing. “America’s missile production hinges on a small number of ammonium perchlorate facilities, meaning a single plant accident can bring output to a standstill; a concentration risk that has no real equivalent elsewhere in the defense industrial base. AP production relies on narrow workforce pipelines for specialized energetics handling, layered environmental and explosives permitting, and purpose-built manufacturing equipment. Each of these inputs is hard to duplicate quickly, even with dedicated funding, which is why decades of rhetoric about supply chain expansion have produced so few second sources in practice.” [Contrary]

Inside Ukraine’s efforts to manufacture thousands of ground-based drones. [TWZ]

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Now that we’ve mapped the contours of the problem, we can begin to explore its deeper nature to understand why, specifically, construction productivity is so resistant to being improved, and why construction costs stubbornly refuse to fall.

We’ll start by looking at one of the most important mechanisms by which production processes can get cheaper: economies of scale. Many processes have lower unit costs as production volumes rise, thanks to a variety of scaling effects: fixed costs can be spread more thinly, equipment gets cheaper on a per-unit basis due to area-volume relationships, improved production methods are developed as a result of learning-by-doing, and so on. However, in construction these effects are modest at best, even in sectors like homebuilding with very large production volumes.

In homebuilding, we’ll see that the limits to these economies of scale are in large part dictated by the nature of the production process. Economies of scale work by eliminating the difference between the costs of the raw inputs to a process and the final costs of production. In a highly efficient, high-volume production process, the costs of the output will gradually approach the costs of the material inputs. But in conventional homebuilding in the US, this difference is already small, giving scale-based strategies relatively little margin to close.

Economies of scale in homebuilding

We’ll examine economies of scale in construction through the lens of housing construction in the US. For many sectors of construction, difficulty in achieving economies of scale could be attributed to the fact that only a small number of buildings of a particular type get built in the US each year. There were, for instance, only 10 skyscrapers taller than 200 meters built in the US in 2025. Semiconductor fabs, urban subways, and airports are similarly built in very small numbers. It’s hard to achieve economies of scale when there’s no scale to be had. Houses, on the other hand, are built in very large numbers. There were over 1.3 million housing starts in the US last year, including 942,000 single-family homes. This isn’t large compared to other types of manufactured goods — the US consumed over 7 billion cans of vegetables in 2025, for instance — but it’s certainly large enough for economies of scale to appear.

However, evidence suggests that homebuilding in the US exhibits relatively modest economies of scale. For one, the level of concentration in the homebuilding industry is relatively small: housing construction in the US is done by a large number of comparatively small firms. A 2022 study by Harvard’s Joint Center for Housing Studies (JCHS) found that the US had over 65,000 firms engaged in homebuilding, and that even the 100 largest firms combined were responsible for less than 50% of the homebuilding market. In homebuilding, the four largest firms held around 18% of the market, compared to 90% in aircraft manufacturing, 86% in wireless telephone service, and 58% in automobile manufacturing. Concentration in the homebuilding industry has been rising over time (primarily due to the growth of the two largest builders, Lennar and D.R. Horton), but it’s still much lower than in many other industries. This level of concentration isn’t what you’d expect to see if economies of scale in homebuilding were substantial.

Another, earlier JCHS study that looked at US homebuilding in the late 1990s and early 2000s found that construction costs were actually higher for the largest US homebuilders than for smaller US homebuilders. It also found that large and small homebuilders had similar gross margins on homebuilding (the difference between the costs to produce a home and what they sold it for). If there were major economies of scale in homebuilding, all else being equal we’d instead expect to see larger firms have lower construction costs and greater gross margins.

More recent data suggest these trends have continued. If we look at the gross margins of public homebuilders, we see virtually no relationship between gross margins and number of homes produced. Lennar, which built over 80,000 homes in 2025, has similar gross margins and average home selling prices to United Homes Group, which sold just 1,192 homes in 2025. LGI Homes and D.R. Horton had nearly identical selling prices and gross margins, but the former sold 4,685 homes in 2025 and the latter sold over 84,000. Looking at different years doesn’t alter the picture much.

This isn’t a smoking gun, as there are other reasons why larger firms might not have higher gross margins than smaller firms. Large firms might be deliberately selling at a lower price to try to capture or maintain market share, as Lennar states it does. But it’s consistent with there being few economies of scale in the homebuilding industry.

In his history of the US homebuilding industry, homebuilder Ned Eichler describes the economies of scale available to homebuilders through the end of the 20th century. While he notes that homebuilders building several hundred homes per year could achieve substantial cost advantages compared to much smaller builders, there appeared to be little advantage in increasing scale beyond that. Writing in the 1980s, Eichler noted that “[n]ot even the largest firm has any of the advantages of dominant companies in other fields… What little technology exists is available to all.”

In the late 1940s and 1950s, as large-scale merchant builders (100 units per year or more) became the dominant suppliers of homes, most of the postwar improvements in production organization and techniques were made. As unit costs began to rise rapidly in the 1960s, because of higher quality, high land and site development costs, and inflation, there was heightened interest in the possibility of another round of increased efficiency from greater scale and technological breakthroughs. Many firms did grow, mostly by geographic expansion. Several broke out of the 100 to 1000 unit range to annual volumes over 5000. By 1980 US Home brought its unit production to 15,000. However, there were no significant changes in technology or even methodology. Nor is there clear evidence that US Home, Ryan, Pulte, or other multicity builders have yet achieved economies of scale.

Today, large homebuilders continue to use the same basic homebuilding methods that much smaller builders do, and builders like Lennar and D.R. Horton effectively subcontract all of their actual homebuilding to local contractors. (Lennar’s 2025 annual report, for instance, notes that “[w]e hire subcontractors for site improvements and virtually all of the work involved in the construction of homes.”) Large homebuilders do note that they’re able to achieve some scale economies by getting volume discounts on material purchases, but evidence suggests that these savings are relatively modest. Group purchasing organizations, such as CBUSA, pool material orders together in order to receive volume discounts from suppliers; CBUSA boasts that it gives independent builders “the purchasing power of a top 10 National Builder.”1 But the savings it offers works out to around $9,500 per house for the builders who use it, or around 6% of the material costs of an average new home.

You sometimes see claims that economies of scale in homebuilding could be achieved with assembly line-style construction, building hundreds or even thousands of homes at once on enormous tracts the way the US did in the 1940s and 50s. But evidence suggests that savings from this style of construction are somewhat modest, and may not materialize at all. Older versions of Craftsman’s National Construction Estimator provide percentage cost deductions for some tasks if they’re done on “tract work,” but these reductions are typically modest (10-20%) and are only available for a small handful of tasks.2 (One exception is carpentry, which does see large labor cost reductions for tract construction in some editions, but this on its own isn’t enough to shift the overall cost of a house substantially.) Likewise, Levitt and Sons famously built thousands of houses in enormous “Levittowns” using a sort of “reverse assembly line”: worker teams would go from house to house, performing a particular set of tasks and then moving on to the next one. But the cost of Levittown houses doesn’t appear to be all that different from those built by other, much smaller homebuilders at the time.

Economies of scale in manufactured homes

We can sharpen our understanding of economies of scale in homebuilding by looking at one particular subset of housing construction: manufactured homes. Manufactured homes, formerly known as mobile homes, are built using largely the same technology as conventional homes, but instead of being built on-site, the homes are produced in a factory, mounted to a steel chassis, and transported to their final location via truck. Roughly 100,000 manufactured homes are produced in the US each year.

Manufactured homes are a useful lens for understanding economies of scale in homebuilding, because they eliminate so many factors that might be expected to restrict them. Economies of scale require repetition — making approximately the same thing over and over again — and such repetition is particularly achievable with manufactured homes:

• Conventional homebuilding is subject to different building code requirements in different jurisdictions, depending on what version of the code has been adopted. But manufactured homes are built to one set of national requirements, the federal HUD code.

• Conventional homes are constructed on-site, and those sites might have substantial variation between them. But manufactured homes are produced repetitively within controlled factory locations.

• Conventional home construction takes place on constantly changing jobsites, with constantly changing construction crews, a constant churn that might disrupt opportunities for learning-by-doing. Manufactured home construction, which takes place within a single location, should be less susceptible to this sort of disruption.

There’s some evidence for greater economies of scale in manufactured home construction compared to conventional homebuilding. Specifically, the manufactured home industry shows substantially higher concentration than the conventional homebuilding industry. The three largest manufactured home producers are Clayton Homes (49,000 units per year), Cavco (about 20,000 units per year), and Champion (roughly 26,000 units per year).3 Together, they make up roughly 90% of the US manufactured home market, similar to the level of concentration we saw in other industries.

However, despite this level of concentration, actual production economies of scale in manufactured home building appear relatively modest. We can see this by looking at the operations of four public manufactured home companies. The first two, Champion and Cavco, produce around 26,000 and 20,000 homes per year, respectively. The third, Legacy Housing, makes around 1,700 manufactured homes a year. And the last, Nobility Homes, makes just a few hundred manufactured homes each year.

Each of these homebuilders is catering to broadly similar segments of the market, since manufactured homes tend to be purchased by lower-income buyers. Their annual reports all note that the typical manufactured home buyer has a low household income, though they disagree on the exact number – Cavco thinks it’s less than $40,000, Champion thinks it’s less than $60,000, and Legacy thinks it’s less than $75,000. As we saw with conventional homebuilders, there doesn’t seem to be much relationship between gross margins on factory homebuilding and production volume. Nobility homes (391 shipments in 2025) and Legacy homes (1703 shipments in 2025) actually have better gross margins on home sales (32% and 28% respectively) than the much larger Champion and Cavco (27% and 23%).

More importantly, all four of these manufacturers use essentially identical production methods. Nobility Homes has one factory in Florida, Legacy has two factories in Texas and one in Georgia, and Champion and Cavco each have several dozen factories spread across different states. These factories are similar: each is typically 100 to 200 thousand square feet, employs 100 to 300 people, and produces several hundred homes a year. In other words, there do not appear to be large economies of scale at the factory level. Even relatively modest production volumes (20,000 units annually) get spread among dozens of different factories. By comparison, a single modern car factory will produce several hundred thousand cars a year.

There are some exceptions to this: Clayton Homes recently announced renovations to an existing factory that will allow it to produce 3,000 homes annually. But overall Clayton fits the basic pattern, as production of its 49,000 homes is spread amongst 41 production facilities.

Within the manufactured home facilities, we see the adoption of various efficiency-enhancing strategies compared to how work would be done on a conventional jobsite. As with Ford’s assembly line, work is arranged so that tasks take place at worker height, eliminating extraneous movements like bending or stretching. With conventional home construction, workers first complete the outside shell of the house to create a waterproof environment to work in, but this means the remaining work must be done within the cramped interior of the partially completed house. Manufactured homes, built under a factory roof, aren’t subject to this restriction and can be built from the inside out. Permanent stations can be set up with equipment for various repetitive operations, such as cabinet assembly. (We see this same sort of strategy adopted in large-block ship construction, with as much work as possible done outside the ship on prefabricated blocks, minimizing the amount of work that needs to be done in the cramped conditions of the assembled ship.)

However, the work remains a highly labor-intensive operation. Workers are in large part doing the same tasks that they would be in conventional construction; those tasks have just been made somewhat easier to perform. Equipment use is likewise somewhat modest: we don’t see the huge arrays of robot arms or automated machinery that we see in car manufacturing or other mass production industries. Per their annual reports, the value of the equipment that Nobility Homes, Legacy Housing, and Cavco operate is just one-third to half the value of the actual buildings. At a car manufacturer like Ford, by contrast, the value of the equipment and tooling is over three times as much as the value of the buildings that contain them.

Part of the reason manufactured homebuilders use a large number of low-capacity factories is high transportation costs. Both Nobility Homes and Cavco note in their annual reports that the cost-effective shipping range for their products is around 350 miles (around a day’s drive). This is a result of dollar density — the value of the product divided by the volume or mass of it. Products with high dollar density, like electronics, are very valuable in proportion to the amount of space they take up and can be effectively shipped long distances; iPhones can be made in a factory in China and then shipped all over the world. Products with low dollar density, like toilet paper, have low prices in proportion to the amount of space they take up, and are harder to ship long distances. If it costs $2,000 to ship a container from China to the US, that $2,000 will be a small fraction of the value of the cargo if the container is filled with iPhones, but a large fraction of the value of the cargo if it’s full of lumber or kitty litter. The lower the dollar density of a product, the less cost-effective it is to ship it long distances. Manufactured homes have very low dollar density, and thus can’t be shipped very far cost effectively.

But transportation costs can only explain part of this distributed manufacturing strategy for manufactured homes. For one, if there were substantial economies of scale at work, it would be worth it for manufacturers to ship longer distances in spite of the higher costs; transportation costs are proportionately high for manufactured homes, but they’re not that high, on the order of a few dollars per mile. Transportation costs thus suggest a sort of ceiling as to the extent of economies of scale that can be achieved for manufactured homes. For another, manufactured home producers often have multiple facilities much closer together than the cost-effective driving distance. Legacy Housing has factories in Commerce, Texas, and Fort Worth, Texas, that are around 100 miles apart. Cavco has three factories in the Phoenix metro area, two factories in North Carolina that are 120 miles apart, and two factories in Georgia that are less than 75 miles apart. If there were substantial economies of scale at the factory level, we’d expect these nearby factories to be combined into larger operations.

Thus even in a homebuilding sector with many tailwinds enabling economies of scale, scaling effects still appear to be relatively minor. Manufactured housing is produced in a controlled factory environment and built to the standards of a single national code, both of which should enable the highly repetitive production necessary for capturing economies of scale. And yet the scale of most production facilities is low, factories employ little in the way of high-volume automated equipment, and very small-scale operators producing a few hundred homes a year appear roughly as competitive as companies producing tens of thousands of homes a year. These patterns suggest that there’s something about the homebuilding process itself that makes capturing economies of scale difficult.

What prevents economies of scale in homebuilding?

The easiest way to understand the difficulties of achieving economies of scale in homebuilding is to look at the relationship between input cost and output cost in various industries.

The graph below shows what’s known as the “crack spread” for gasoline production in the US Gulf Coast between 2014 and 2019. The crack spread is the difference between the price of crude oil and the wholesale price of gasoline. It essentially tells us how much margin oil refineries have to work with — how much they can afford to spend on the various production processes per barrel of oil processed. We can see that the crack spread for gasoline was typically around $15 a barrel for most of this period, compared to a crude oil price of around $50-70. In other words, the ratio of the price of wholesale gasoline to the price of crude oil was around 1.2 to 1.3.

An oil refinery is an enormous, expensive process facility that costs billions of dollars to construct. But because refineries produce such a large amount of gasoline and other petrochemicals, this cost can be spread very thinly across the refinery’s output, and the price of the final products is not all that much higher than the costs of the material inputs.

Likewise, consider car manufacturing. A few years ago, car industry analyst Munro & Associates conducted an analysis of the production costs of BMW’s i3 electric car. The report breaks down the various costs of production for different parts of the car, letting you see the cost of the inputs, the number and cost of the various operations performed on it, and the cost of the completed assembly.

We can see that the ratio of output costs to material input costs is higher than with gasoline production, around 1.8 on average. For complex manufactured goods that require a lot of distinct operations (the motor assembly alone has almost 5,000 parts and almost 30,000 assembly steps), it’s hard to achieve an output-to-input ratio as low as can be seen in a scaled continuous chemical production process.

More generally, any factory or production process will take some physical inputs, perform some set of transformations on them, and produce some series of outputs. If those transformations are involved or complex, requiring a lot of expensive machinery or labor-intensive operations, the ratio of the cost of the outputs to the cost of the inputs might be quite high. But even with a large number of complicated steps or very expensive equipment, this ratio can be driven down by taking advantage of economies of scale: spreading the fixed costs of your equipment out over a larger production volume, or using higher-capacity equipment or processes that has lower per-unit costs. As production volumes rise and a process gets more efficient, the ratio of the output costs to the input costs should theoretically approach one.

Elon Musk calls the ratio of the final cost of a product to the cost of its raw materials the “idiot index.” A high idiot index suggests a highly inefficient process with a lot of wasteful operations that could be dramatically improved. A low idiot index suggests a process with much less room for substantial improvements. (It was allegedly the high idiot index of rockets that convinced Musk he could beat existing aerospace companies and that SpaceX was worth pursuing.)4

Armed with this concept, let’s look at the cost breakdown of a typical US single-family home, courtesy of Craftsman’s National Construction Estimator. The chart below shows the hard costs of construction: what it takes to put up the actual, physical building.

The most important takeaway here is that the hard costs of constructing a single-family home are roughly 50% labor and 50% materials. This means that the ratio of the costs of the output to the costs of the physical inputs (drywall, lumber, concrete, etc.) is around 2. This is a very low ratio: it’s only slightly higher than the ratio we saw with various BMW i3 subassemblies.

With car manufacturing, this low ratio is achieved by way of scale, spreading the costs of expensive equipment and hundreds of workers over a very large output. If BMW tried to assemble its i3 cars one at a time, outside of a factory, the output-to-input ratio and the overall costs would be vastly higher, the same way that the craft method of car building was far more expensive than Ford’s mass production methods.

In home construction, however, this low ratio is a product of the fact that the amount of transformation taking place on the inputs is relatively modest. It takes a relatively small amount of labor and equipment to transform lumber, drywall, concrete, shingles, and other building materials into a finished home.

The fact that the ratio between costs of constructing a home and the costs of the various materials is already so low is fundamentally what makes achieving substantial economies of scale difficult. For complex manufactured goods, when this ratio is high — when there are a lot of labor-intensive operations or transformations required to turn input materials into a final product — the strategy for efficiency improvement is relatively straightforward. Build some machine to perform some particular task, sequence it together with other machines performing different tasks, and then use those machines to produce as much output as possible. The machines might be expensive to develop and build, and you’ll need new, expensive workers to keep the automated systems running. But with high production volumes this trade-off is worth it: all these costs get spread out thinly, and the ratio of output costs to input costs trends downward. This is broadly what Ford did for production of the Model T, and it’s how production for things like lightbulbs evolved in the early 20th century.

But when the ratio between output costs and input costs starts out low, as it does with homebuilding, it’s hard to take advantage of this strategy: the potential savings are just too small. I’ve noted previously that it takes a lot of expensive automation to duplicate the efforts of a small number of construction workers with hand tools, and when I was at Katerra, executives would often bemoan how hard it was for their capital-intensive production to compete with “Bubba and his truck” — low overhead contractors using manual labor and not much else. The ratio of output costs to input costs doesn’t give enough “bite” for production economies of scale to act on, and there’s little to be gained in production economies by dramatically increasing the scale of your homebuilding operation.

Conclusion

Economies of scale are often one of the most powerful forces available for making some process more efficient. But economies of scale operate on the difference between the costs of the raw inputs to some process and the costs of the final product. When this difference is large, there are often substantial economies of scale that can be achieved. You can introduce automated equipment or other large fixed costs that reduce your overhead costs when spread over a large enough production volume. You can use larger-capacity equipment that has lower per-unit costs than smaller-capacity equipment. You can gradually make some process more efficient via learning-by-doing. All these strategies will whittle away the difference between your raw input costs and the costs of your final product.

But when this ratio is already low, economies of scale have little room to operate. Thanks to the relatively modest requirements for transforming construction materials into finished houses, homebuilding has the sort of ratio of output costs to input costs observed in high-volume production operations like car manufacturing. Once the low-hanging fruit is captured, such as more efficiently arranging the work in manufactured home factories, there’s little production efficiency to be gained from further increases in scale.

Thinking about economies of scale in terms of the ratio of output costs to material input costs does suggest strategies for achieving them:

• You could find sectors of construction, such as nuclear power, where the ratio of output costs to input costs is very high, and thus have more opportunities for efficiency improvements.

• You could vertically integrate backwards into the production of raw materials and components, in the hopes of driving down those costs.

• You could find a way to use fewer and/or cheaper raw materials.

We’ll look at the viability of these strategies in future essays.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure and industrial technology. This week we look at squatter removal services, Apple finding uses for defective chips, process heat use in California, the brewing Colorado River crisis, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

War in Iran

Iran wants to charge US tech companies for use of the undersea cables that pass through the Strait of Hormuz. [Ars Technica] And Iran is starting a Bitcoin-backed ship insurance service [Bloomberg]

The hundreds of ships stuck in the Persian Gulf are starting to get damaged by all the barnacles and jellyfish that they’re accumulating. “...the Gulf’s shallow sandy seabed and warm waters have put ships at anchor or adrift there at risk of sand and sea creatures clogging up gratings that protect the vessel’s internal pipework. Seafarers are also struggling to get hold of critical parts when systems have broken down.” [FT]

An interesting example of the complexity of petroleum and petrochemical supply chains: a cooking oil shortage in India is driving up gas prices in California. “India, the most populous country, uses LPG as its primary cooking fuel. Cut off from Middle Eastern LPG, ​which represented over 90% of India’s total imports of the fuel before the Iran war, New Delhi has directed refiners to maximize LPG output. To comply, refiners have cut production of alkylates - motor fuel ​additives made using LPG as feedstock. For California, shrinking alkylate supply compounds concerns of a potential gasoline shortage due to declining fuel production and exports from ⁠Asian refiners struggling to access Middle Eastern crude oil. Alkylates are highly sought in California because they burn cleaner than other additives, and the state requires a unique gasoline blend to reduce smog.” [Reuters]

The US has lost 42 aircraft so far in the war with Iran, most of which (24) are MQ-9 Reaper drones. [National Security Journal]

Housing

The House passes its version of the Senate’s Road to Housing Act, without the build-to-rent restriction, 396 to 13. [NPR]

The NYT has a big, data-driven opinion piece on the need for the US to build more homes to bring down the price of housing. “The situation in expensive coastal areas, however, is far worse. They have enacted onerous zoning and building rules that limit home construction. They have allowed the “not in my backyard” instinct to prevail. Many of these areas vote Democratic and identify as politically progressive, yet their housing policies have increased inequality. By maximizing home prices, these parts of blue America have benefited existing homeowners, who tend to be older and richer, at the expense of everyone else. Nationwide, the relationship between home prices and home construction is even stronger than many Americans realize.”

Because California makes it so difficult to remove people living in a residence (whether they’re there legally or not), some California landlords are hiring “squatter removal services” to intimidate illegal squatters from vacating the properties they’re occupying. “Jacobs claims to have developed a long list of tools and tactics that enable him to remove squatters far faster than the court system, all while staying within the bounds of the law. Chief among them is a weapon he carries on every job: a katana, a curved Japanese sword that’s more synonymous with samurai warriors than clearing squatters. “In most industries, swords just don’t make any damn sense,” Jacobs says. “In this particular one, it actually does.” The lightly regulated katana, he explains, is an ideal weapon for indoor self-defense and intimidation.” [Reason]

Washington state legalizes scissor stairs, interlocking stairways that let you combine two stairways in one shaft. Scissor stairs are, like single-stair apartment buildings, a space-saving building design feature that’s common in other countries but mostly illegal in the US. [Sightline]

Manufacturing

Because of their microscopic size and their sensitivity to minute changes in chemical concentrations, semiconductor fabricators invest an enormous amount of effort into process control to minimize the number of defects that occur on a microchip. Defects nevertheless still occur, and when they do there’s often no real way to repair them. But that doesn’t mean the entire chip is useless; it just means you need to find a way to use it without the damaged areas. “The chip powering the Neo is Apple’s A18 Pro, the same chip first used inside the iPhone 16 Pro two years ago, but with one key difference. The Neo version of the chip has a “5-core” graphics processor, one less than the version inside the 2024 iPhones, indicating that Apple was able to save some of the A18 Pro chips with a defective core for future use. Defective cores can be disabled, leaving a chip that still functions perfectly well to power different, often cheaper devices—in this case an entry-level laptop instead of a top-of-the-line iPhone. It is the latest example of Apple deploying a decades-old chip industry strategy to squeeze profits from lesser-performing processors by selling them like eggs, gas, diamonds or hotel rooms, segmented by good, better and best.” [WSJ]

Read more

The build-to-rent, or BTR, industry has been in the spotlight recently because of a major federal housing bill, the Senate’s 21st Century ROAD to Housing Act. This bill, which is ostensibly designed to stimulate the building of new homes, includes a provision aimed at preventing large institutional investors from owning single-family homes. This provision, section 901, requires institutional investors (companies that own more than 350 single-family homes) to sell any build-to-rent homes to individual homeowners after seven years. Because BTR involves building a home and then retaining ownership of it to rent out, this provision threatens the fundamental business model of the BTR industry. Since the announcement of this provision, funding for new BTR projects has virtually ground to a halt while investors wait to see whether the bill actually passes. Over 100 pro-housing groups, including Berkeley’s Terner Center, the NAHB, and my colleagues at IFP have come out against this provision specifically, on the grounds that it’s likely to significantly reduce housing supply in the short term.

Because BTR has quickly become such a large fraction of new home construction and is now in the policy spotlight, it’s worth understanding the origins of the industry and why it has become so popular.

Origins of BTR

The modern BTR industry, where developers build entire communities consisting of dozens or hundreds of single-family homes for rent, is a product of the 2008 Global Financial Crisis. Prior to the financial crisis, single-family home rental wasn’t uncommon — in 2005, there were over 8 million detached single-family homes being rented — but the business was mostly the purview of small “mom and pop” operators that owned a relatively small number of scattered rental properties. As late as 2011, no single company owned more than 1,000 rental homes in the US.

But the financial crisis shifted the housing landscape. Huge numbers of people lost their homes to foreclosure: foreclosure rates in 2009 and 2010 were four times rates from 2005, and between 2007 and 2010, there were four million foreclosures. The homeownership rate in the US fell from a high of 69% in 2005 to 63% in 2016. At the same time, to rein in the subprime lending that had precipitated the crisis, banks tightened their lending standards, and average mortgage credit scores rose by more than 50 points. In 2003 buyers with a credit score of less than 620 made up 7% of all mortgages. By 2011 that had fallen to essentially zero.

The raft of foreclosures and the tightening of lending standards had two simultaneous effects on the housing market.

First, they pushed millions of Americans into renting. Between 2010 and 2015 the number of renter households in the US rose by roughly six million, while the number of homeowner households declined by roughly 800,000.

Second, this shift created a huge pool of homes available for purchase at very low prices. Between 2006 and 2010 the value of US homes dropped by 26%, greater than the average decline during the Great Depression. In some markets the declines were even worse: home prices declined by 60% in Las Vegas, and by roughly 50% in Phoenix, Miami, and Tampa.

In response to these market conditions — millions of homes available to buy cheaply, and millions of Americans who couldn’t afford to buy them — various real estate ventures were formed to take advantage of the situation. In 2010, the Arizona-based housing investment company Treehouse Group began to buy distressed mortgages in Phoenix and turn them into rental housing. Within a year the company had purchased 11,000 homes. In 2012 Treehouse was acquired by the investment group Blackstone, which turned Treehouse into the single-family rental company Invitation Homes. Today, Invitation Homes is one of the largest home rental companies in the US, with more than 86,000 rental homes across 12 states.

In 2012, the same year Treehouse was acquired, Wayne Hughes, founder of self-storage company Public Storage, founded American Homes 4 Rent, which similarly began to buy distressed mortgages and turn them into rental properties. Today, American Homes 4 Rent owns 61,000 rental homes across 24 states. 2012 was also when Tricon, a Canadian real estate company formed in 1988, began to buy up distressed mortgages for rental; by 2023, the company owned more than 38,000 of them. While prior to 2011 no single company owned more than 1,000 rental homes, by 2022 the four largest home rental companies owned more than 200,000 of them, largely concentrated in the Sun Belt and Western US.

This large-scale acquisition and transformation of single-family homes into rental properties was encouraged by the federal government, as part of broader efforts to keep the housing market from collapsing completely. In 2012 the Federal Housing Finance Agency launched the REO-to-Rental Initiative pilot program, which “allowed pre-qualified investors to bid on large portfolios of foreclosed properties owned by Fannie Mae.” Roughly 1,800 homes were sold to investors under this program. And in 2017, Fannie Mae backed a billion-dollar loan to Invitation Homes for the purposes of purchasing rental properties.

While most of the new entrants into the home-rental market focused on buying distressed properties, renovating them, and then renting them out, a few companies pursued a strategy of building rental homes directly. NexMetro, founded in Phoenix in 2012, completed its first community of single-family homes built specifically for rent in 2015, and today has built more than 10,000 rental homes across 50+ projects under its “Avilla Homes” brand. BB Living, another Phoenix-based company, also began building rental home communities in 2012, and today operates 18 of them. AHV Communities began construction on its first BTR project in Texas in 2014, and over the next 12 years would build three dozen more.

Building new rental homes had several advantages compared to acquiring existing homes. Being new construction, they typically had much lower maintenance costs than existing homes, and they could be designed by the developer with an eye towards minimizing maintenance and overheads. And because they were clustered together, they were somewhat easier to manage than purchased rental houses that might be spread across a wider area.

As the housing market recovered and the pool of single-family homes available for purchase at favorable prices dwindled, many of the large home rental companies began to experiment with their own BTR strategies. American Homes 4 Rent began work on its first ground-up rental community in 2016; today it owns more than 14,000 BTR homes, with essentially all new home acquisition coming through BTR. Invitation Homes began purchasing BTR homes in 2021 in a partnership with homebuilder Pulte, and as with American Homes 4 Rent essentially all its home acquisition now comes from BTR. Pretium Partners, which owns over 80,000 single-family homes under its “Progress Residential” umbrella, formed a $1 billion BTR venture in 2021. Some companies, such as American Homes 4 Rent, opted to do all their BTR development work in-house, while others preferred to partner with existing homebuilders, buying new houses that developers constructed in bulk.

BTR has attracted the attention of several large homebuilders. Lennar, the second largest homebuilder in the US, began building BTR communities in 2020 under its Quarterra subsidiary (though it has since sold a majority stake in this operation). DR Horton, the largest homebuilder in the US, began building BTR communities in 2019, and last year sold nearly 3,500 BTR homes. Taylor Morrison, the eighth largest homebuilder in the US, launched a BTR brand, “Yardly,” in 2022. BTR can be attractive for homebuilders because it provides them a large stream of steady work and can give them a sort of “escape hatch” if units seem to be selling to homeowners less well than anticipated. (Though apparently some homebuilders refuse to work with BTR companies because they don’t want to be associated with rental housing).

Today, BTR is still a small segment of the overall housing market: CBRE estimates that there are about 350,000 BTR units in the US, which is just 1.5% of the overall single-family home rental market. But it’s a rapidly growing segment of the US housing market — or was, until this recent Senate bill.

What sort of homes are BTR?

“Build-to-Rent” has become synonymous with single-family homes built specifically to be rented out, typically in communities of a few dozen to a few hundred rental homes, but within that category companies offer a broad range of different products. BTR generally gets broken down into several major subcategories (though some use slightly different ones): single-family detached, single-family attached, and horizontal multifamily. These categories exist on something of a spectrum of “very similar to conventional single-family homes” on one end and “very similar to conventional apartment buildings” on the other.

On the single-family home end of the spectrum, you have BTR communities such as Tricon’s Palomino Ranch, built in Houston in 2019. These BTR homes are essentially indistinguishable from single-family homes built for purchase: they’re detached homes with their own garages, driveways, and large fenced backyards. These sorts of homes will be “individually platted”: placed on their own individual plot of land with their own unique address.

On the apartment end of the spectrum, you have what’s known as “horizontal multifamily.” These are BTR developments that are essentially spread out apartment buildings. They are built in a range of sizes typically associated with apartment buildings rather than houses, with 1-, 2-, and 3-bedroom options, and they’re platted together on one large lot. They might have apartment-complex style amenities (gym, clubhouse, etc.), and will typically lack attached garages. They’re attractive to tenants because they don’t have shared walls and they have individual backyards, and they tend to rent for 15–20% more per square foot than a comparable apartment, but in many ways they’re closer to apartment complexes than they are to single-family homes. NexMetro’s “Avilla Homes Deer Valley” community in Phoenix is an example of this sort of BTR development.

Between these ends of the spectrum you have the “single-family attached” category — single-family homes that share one or more walls, such as townhomes or duplexes. BB Living’s Val Vista community of townhouses in Gilbert, Arizona is an example of this sort of project.

Regardless of the layout, a BTR community is at its core a large rental development and is designed by the builder to maximize rents and minimize overhead costs. For conventional homes built for sale, for instance, a builder might simply specify whatever finishes and features a buyer wants. But for a property that the developer is going to hold as a rental, choices are made with an eye towards minimizing maintenance costs by using cheaper, more durable finishes. American Homes 4 Rent, for instance, has stated that due to its designs being optimized for low-maintenance, maintenance costs are just 25% of what they were in their purchased homes.

Home rental companies also have other strategies for trying to minimize their overhead costs. Property taxes, for instance, are often the largest single operational expense for rental home companies. American Homes 4 Rent notes that it’s able to minimize its property tax costs by systematically filing property tax appeals to try and reduce the appraised value of its homes; a 2022 report noted that the company had a dedicated property tax team that filed more than 25,000 appeals annually.

Construction of BTR homes is in large part concentrated in Sun Belt states that have large populations, relatively high growth rates, and space to build new housing developments: Texas, Florida, and Arizona are the top three states for BTR construction, with Georgia and North Carolina rounding out the top five. An estimated 60% of BTR construction takes place in these five states. Within these states, BTR tends to be concentrated in large metro areas: most of Arizona’s BTR takes place in Phoenix, most of Georgia’s BTR is being constructed in Atlanta, and most of Texas’s BTR is being built in Dallas and Houston.

What’s driving BTR demand?

The largest factor driving demand for rental housing, both build-to-rent and buy-to-rent, seems to be housing affordability. Since 2012 home prices in the US have risen faster than inflation and median income. The post-Covid period in particular saw a dramatic increase in home prices, along with a large rise in interest rates that substantially raised the cost of mortgage payments and often made renting more financially appealing than buying. At the same time, Covid drove large increases in the frequency of working from home, which in turn created a demand for larger amounts of living space.

Americans who want more space or more privacy than apartments offer, but can’t afford to purchase a new home, will naturally turn to renting them. Amherst Group, which collectively owns over 50,000 single-family rentals (though not, as far as I can tell, BTR homes), noted in 2021 that 85% of its residents would not qualify for a mortgage due to low credit scores and incomes. NexMetro’s 2024 investor notes the largest factors driving demand for rental housing are rising interest rates and overall home affordability. American Homes 4 Rent’s 2025 annual report states that it has benefitted from “the surge in demand for larger living spaces, and increases in mortgage rates which have made home ownership more expensive.” One BTR developer I spoke to said that many residents simply don’t have the down payment that would be needed to afford a home purchase, and that BTR can give residents a way to access highly desirable school districts that would otherwise be totally unaffordable.

But while affordability issues seem to be the primary driver of BTR’s popularity, there also seems to be some fraction of residents that simply prefer renting over owning, due to a desire for less maintenance or simply because they don’t perceive owning a home as a major life goal. CBRE, NAHB, and NexMetro all mention various demographics of “renters by choice” (such as retirees), an analysis echoed by several BTR developers I talked to. As construction of BTR communities continues, this growth might create a sort of reinforcing cycle: more people move into rental housing, which makes it more accepted, which draws even more people in, and so on.

What’s BTR’s effect on the housing market and home prices?

The effect of large institutional investors (large companies that own thousands of homes) on the housing market has become a major point of controversy. Many, many people have the intuition that companies buying up or otherwise owning large numbers of single-family homes drives up the price of housing, and banning or curtailing this activity has become a popular cause on both the right and the left.

The intuition isn’t crazy. In a normal market, where producers aren’t limited in how much of a product they can make, this sort of purchasing wouldn’t drive up prices because it wouldn’t affect supply in a meaningful way. No one worries about Hertz driving up the cost of cars by buying huge numbers of them to rent out. But housing isn’t a normal market, and in many parts of the US it’s unreasonably difficult to create new housing supply. Ire at large, institutional homeowners would perhaps be more profitably directed at the various supply constraints that make it hard to build new housing in the first place (Invitation Homes notes that it benefits from operating in markets with “high barriers to entry”), but in a world where those supply constraints aren’t going to be removed any time soon, it’s worth considering what effect large rental companies are having on the market.

Supporters of rental housing are quick to point out that institutional owners make up a very small fraction of the housing market, less than 1% of single-family homes. However, because institutional ownership is highly concentrated in a few Sun Belt cities, in some housing markets institutional ownership can make up a substantial fraction (up to 25%) of the rental housing market.

There’s some evidence that institutional ownership might drive up home prices. Mills et al. (2015) noted that large scale buy-to-rent investors entering a market tended to “support” (i.e., prevent from falling) house prices in the short term. Lambie-Hanson et al. (2019) estimated that “the increasing presence of institutions in the housing market explains over half of the increase in real house price appreciation rates between 2006 and 2014.” D’Lima and Schultz (2020), Oosthuizen (2023), Coven (2025), Wang and Zhai (2026), Barbieri and Dobbels (2026) and Gorback et al. (2024) all found broadly similar results. However, Hanson (2024) finds that while house prices rise when institutional investors enter a market, this rise can mostly be attributed to improving market conditions, and that prices would have increased whether institutional investors entered the market or not.

The evidence for the effects of institutional ownership on rents, however, is somewhat different. Large owners might use their market power to raise prices (large rental companies will basically admit they raise rents compared to what mom and pop renters would charge), but by buying properties and turning them into rentals investors are also adding supply to the rental housing market, which all else being equal should bring rents down. Lee and Wylie (2024) find that investors raise rents at above average rates when first entering a market. Gurun et al. (2023) found that “institutional landlords leverage their market power to extract greater surplus from renters.” And Hanson (2024) finds that rent growth is due to the market power of the institutional investors. On the other hand, Coven’s (2025) model based on various census and investor property data suggests that added supply from institutional investors can reduce the rent paid by renters, and Wang and Zhai (2026) find that “the majority of renters gain from the expanded rental supply.” Barbieri and Dobbels (2026) find that while the market power of institutional owners raises rents, the increase in supply more than makes up for it, causing rents to fall on net. Economics student Nicholas Decker looks at what he thinks are the strongest papers, and concludes that the net effect of institutional investors entering a market is positive for consumers overall, with the benefits of lowered rents outweighing the increase in house prices.

However, these studies are all for buy-to-rent homeowners: investors buying up homes that already exist. None of them looked at build-to-rent, which we might expect to have substantially different effects: buy-to-rent reallocates existing supply, whereas build-to-rent creates new supply (though it could theoretically also reduce the supply of homes for sale if, in the absence of BTR, for-sale housing projects would be built in the same location).

Unfortunately, the economic research around the effect of BTR specifically is much thinner, and there haven’t been robust studies of the effect of BTR on the housing market specifically.

Conclusion

Overall, I think there are a few points worth making about BTR.

One is that, given that the attractiveness of owning vs. renting can vary depending on the person and the state of the housing market, making a broader array of rental options available for people is a positive thing. I remember years ago when I was trying to find a large (3+ bedroom) place to rent, and how difficult it was to find rentals that size in the area I was looking in. Giving people more ways to purchase housing is good, the same way it’s good for people to have the option to buy or lease a new car.

Second, it’s clear that many folks strongly believe that large-scale corporate ownership of rental housing (which would include BTR communities) is something that can have negative effects on the housing market. But I think it’s more useful to think of the popularity of rental housing as something that’s a product of the housing market: it’s a natural consequence of housing getting increasingly unaffordable thanks to high interest rates and skyrocketing housing prices. Shutting down BTR is a poor way to address that problem; what we need to do is build more housing and develop construction methods that let us construct buildings more cheaply.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure and industrial technology. This week we look at Tokyo’s cheap housing and expensive land, the House response to the Senate housing bill, an IED near an Alabama dam, Fervo’s IPO, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housekeeping items:

• No essay this week because of a respiratory illness that is carving through our household like Sherman through Georgia, but I’m working on a piece about the build to rent housing industry that should be out next week.

• I gave a talk about why major building collapse is so uncommon at Antithesis’ Bug Bash a few weeks ago. You can watch the talk here.

War in Iran

The Economist on why the closure of the Strait of Hormuz doesn’t seem to have yet created a worldwide oil crisis: other exporters (like the US) have raised their output, and many larger importers (like China) are importing less. [The Economist]

On the other hand, it seems like supplies of various petroleum products are starting to get thin. The owner of an oil change company is claiming that US consumer motor oil supplies are going to start getting thin in a few weeks. [X] And European jet fuel stockpiles are apparently almost exhausted. [X]

Some Japanese snack manufacturers are changing to black and white packaging due to ink supply disruptions. [AP News] The disruptions are due to a shortage of naphtha, a light fraction of crude oil. Interrupted naphtha supplies are also apparently poised to cause disruptions to the Japanese semiconductor industry. [Threads]

The UAE has started to build large anti-drone cages around its infrastructure to protect it from Iranian drone attacks. [The War Zone] And it’s also building a second oil pipeline to increase how much oil it can export outside the Strait. [X]

Thanks to Trump’s Jones Act Waiver, there have now been at least 45 cargo trips between US ports via non-US ships. [Cato]

Housing

Alex Armlovich on how making it easier to build housing both reduces rents and increases land values in the city of Tokyo. “The famous (relative) cheapness of Tokyo housing is not a story about cheap land. A one-acre detached house in central Tokyo would cost more than $100 million in dirt before you broke ground. The land is pricey but the structures are cheap. Admittedly, Tokyo’s rents and prices are not as cheap per square foot as buildings in the US Sunbelt’s midsize cities, but cheap by the standards of any 10-million-plus Anglosphere metro area. Tokyo built its way to relative affordability without ending up with low land values, and the values themselves look reasonable for a productive, agglomerated megacity that simply didn’t artificially restrict its own supply.” [Abundance and Growth]

Ezra Klein moderates a debate on housing policy between the leading Democrat candidates for Governor. [YouTube] And CA Yimby declined to endorse any candidate for governor, not because none of them had good housing plans, but because all of them did. [Cal Matters]

The house comes out with its response to the Senate’s 21st Century ROAD to Housing Act. The house bill notably does not contain the onerous restrictions on build-to-rent housing that the Senate bill does. [Politico]

Are homes sitting empty because it’s too expensive for people to sell them? “According to Flock Homes, for many retirees “the tax bill triggered by a sale far exceeds the cost of simply leaving the home empty.” This includes capital gains taxes (paid on the profit from the sale) and depreciation recapture taxes (which sellers of rental properties face). The federal capital gains exemption, which applies to the sale of primary residences, was not factored into the analysis of vacant homes. Mortgage payments were not included in the analysis, since many older homeowners own their homes outright.” [NYT]

Number of sellers vs number of buyers in the US housing market. [X]

Manufacturing

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Welcome to the reading list, a list of news and links related to buildings, infrastructure, and industrial technology. This week we look at trapped buildings, in-home data centers, cardboard military drones, Brightline’s potential bankruptcy, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housekeeping items:

In this week’s newsletter about how early inventions could have appeared, I flagged the safety pin as an invention that could have been invented much earlier, and would have been useful had someone thought of it. The major inventions list (which I sourced from Wikipedia) lists the invention date for the safety pin as 1849, but Claude thought there was no reason that the safety pin couldn’t have been invented as early as 500 BC. Turns out Claude was right, and safety pin-like devices were around 3000 years ago.

This raises my confidence in the rest of Claude’s timeline estimates.

War in Iran

Amazon says that repairs to its damaged Middle East cloud operations could take months. [Reuters] And Iran attacked a UAE petroleum complex. [Reuters]

The closure of the Strait of Hormuz has caused investors to funnel more funding into clean energy stocks. [FT]

Due to the risk of damage to undersea cables going through the Strait of Hormuz, hyperscalars like Google and Microsoft are buying capacity on fiber-optic cables strung alongside Iraqi oil pipelines as a backup. [Rest of World]

Housing

How many buildings are “trapped buildings” — buildings that wouldn’t be allowed to be rebuilt because they don’t meet modern code requirements, but also can’t be torn down or substantially modified because of historic preservation laws? [X]

Using AI as an interior design tool predates the modern AI revolution — I believe AI tools were being used for “virtual staging” of real estate listings before the rise of LLMs — but it’s worth keeping an eye on its progress. Homedesign.AI lets you reimagine some interior space by uploading a picture and having the AI modify it. [HomeDesigns AI]

I had to see this news item three or four times before I realized it was serious and not someone doing a bit. A startup is working on micro-data centers that can be installed in people’s homes, taking advantage of their extra electricity capacity. “Span is a California-based startup that originally launched with “smart” electrical panels designed to help homeowners save money on their electricity bills. Now, with the help of Nvidia, it has come up with something new — small, fractional data centers, or “nodes,” called XFRA units, that can be put on the side of residential homes and small commercial businesses. The idea is to take advantage of unused electrical capacity on local grids, which the Span smart panels can pinpoint.” [CNBC]

Manufacturing

The Wall Street Journal has a piece on Ford’s efforts to build a $30k electric truck, which despite the company’s enormous losses on EVs is still going forward. “With its new truck, Ford says it has eliminated thousands of feet of heavy copper wiring, cut out hundreds of parts and made it 15% more aerodynamic than its other pickups. The process included rethinking the assembly line, which Ford helped to pioneer. That process is traditionally iterative, slow and depends on scores of outside partners. On Ford’s new “assembly tree,” a modular system stamps out two massive, aluminum castings and a battery that get merged at the end of the process—closer to how Tesla and China’s automakers build EVs. “We’ve never blown the whole thing up before and just started over,” Coffey said. “If and when we build this, we will rewire Ford.”” [WSJ] And another similar article in the New York Times. [NYT] Presumably some PR firm is earning their keep getting these articles placed.

There’s lots of debate about how fast the economy might grow if we truly develop Artificial General Intelligence (AGI) that can do anything a person can do. The big divide in this debate tends to be between the economists, who think growth rates might be a few percentage points, and the technologists, who think growth rates might be vastly higher. Here’s an interesting contribution to the debate, an estimate of how fast the economy could theoretically grow based on analysis of actual industrial production rates. “In this post, I compute the maximum rate at which an autonomous AI economy could grow, once its production is concentrated in the sectors most important for self-replication. I take the conservative case for this calculation: full automation, but no other technological improvement. Using US input-output data, I find this economy could double in about a year…” One interesting item in this analysis: under this rate of growth, construction would be a much larger share of GDP than it is currently. [Defenses in Depth]

The NYT on Geely, the Chinese car manufacturer jockeying with giant BYD. “In an unexpected development, Geely beat BYD in sales in the first two months of the year and is rapidly broadening its lineup. Geely is now pushing overseas, more than doubling exports to Europe, the Middle East and elsewhere in the past year and taking on global rivals on their home turf.” [NYT]

Read more

Stimulated emission had been known to physicists for over 30 years, and “regenerative” oscillators, that is, oscillators with feedback, were well known to engineers. Why, then, was Towne’s insight so novel? The answer appears to be that in 1951, physicists and engineers in the United States were not yet sufficiently acquainted with each other’s territory to find it natural to put the two ideas together.

This sort of decades-long wait between when a technology first becomes possible, and when it actually appears, seems common, or at least seems like it might be common. I’ve previously written about why it took so long for wind power to be widely deployed after it became technologically possible, and people often idly speculate whether inventors in the Roman Empire could have built a steam engine, or why we waited so long to put wheels on luggage.

Knowing how long this gap between when an invention becomes possible, and when it actually appears, is useful, because it tells us something about the nature of technology and technological progress. What factors govern whether some new technology appears? How much does mere technical possibility matter, and how much do things like cross-pollination of knowledge, economic feasibility, and political factors contribute? Knowing more about how long it takes for an invention to appear once it becomes technically possible can help us answer these sorts of questions.

I wanted a better sense of how long it takes for some technology to appear once its necessary predecessors are in place. So I used AI to try and find out.

Method

To do this, I used a list of 190 major inventions that I’ve used for previous analyses of technology. For each invention, I asked Claude Opus 4.7 how much earlier it could have been invented.

This required pinning down what exactly I mean by “could have been invented.” For one, it’s often possible to build a working example of some technology long before it’s capable of solving a real problem. Working incandescent light bulbs were built decades before Edison, but they weren’t useful for providing indoor illumination until Edison developed one that lasted for many hours without blackening or burning out. For another, it’s often possible to build something before the problem that it solves has been articulated. Surgical masks — a cloth covering over the face — could have been invented thousands of years ago, but inventing them only makes sense once the germ theory of disease has been articulated.

Nonetheless, I decided to use “could a working example of this technology be built” as my meaning, ignoring whether that technology would be practical or economically useful. In part I chose this criterion to follow the contours of the inventions list, which for the most part is when things were first invented, not when they first improved to the point of becoming practical for regular use. For instance, the list includes the ballpoint pen as being invented in 1888 by John Loud, even though practical ballpoint pens didn’t appear until the 1930s. I also chose this criterion because pinning down technical possibility seemed difficult enough without also having to consider questions like “would someone in this time period be willing to pay for a technology that worked roughly this well?”, which seems like a much harder question.

I specified “could a working example of this technology be built” as follows: I asked Claude to assume an inventor working in a well-equipped, era-appropriate workshop with a team of highly skilled engineers and craftsmen. Could they, using knowledge and technology available at the time, build a working example of the technology in five years?

I allowed the hypothetical team to build one required precursor technology, if that technology was simple enough that the team could plausibly build it along with the invention in question. For instance, Edison’s light bulb was predicated on the demonstration of Sprengel’s 1865 mercury vacuum pump, using those insights to achieve vacuum high enough that the filament wouldn’t burn out quickly. However, a Sprengel mercury pump is not amazingly complicated — it works by dropping mercury through a glass tube, forcing air out along with it — and it’s plausible that a team working to build a better incandescent lamp could have invented it as a collateral innovation as part of their improvement efforts. (Edison, after all, had to invent lots of other technology — generators, distribution systems, etc. — along with his bulb to make it practical.)

I also allowed the fictional team to generate new knowledge through iteration and engineering-style experimentation, but I did not allow them to discover new scientific frameworks or make novel key empirical observations guided purely by scientific curiosity. So a team trying to build an electric motor in the early 19th century in this simulation could not just summon up Oersted’s critical observation that electric current creates a magnetic field, and a team trying to invent the transistor in the early 20th century would not have access to the band theory of quantum mechanics.

You can read the entire prompt I used here.

For the output, I had Claude list a range encompassing two dates. The first is the earliest plausible date for when the inventor’s team could have succeeded with some charitable assumptions and a bit of luck. The second is the straightforward date, when multiple independent teams would be likely to converge on a working model of the invention. Along with the date range, I had Claude list the necessary prerequisite technologies and scientific knowledge, and give a short explanation of its reasoning. An example of the output from Claude for an invention is below:

Claude gave ranges for 166 of the 190 inventions. The other 24 it flagged, mostly either because it was a scientific discovery rather than an invention (like X-rays) or because its real-life invention was a serendipitous accident that couldn’t be expected to be recreated earlier (like Perkin’s invention of mauve dye). You can read the full document with all Claude’s answers here.

One issue I noticed reading through the answers is that I didn’t do quite enough to pin down cases like the surgical mask, which are gated almost entirely by conceptions of the problem. There’re a few other items like this, notably Morse Code and Braille, and while Claude did a reasonable job of handling them (it gave answers for Morse Code and the surgical mask which assumed that they needed to wait until the problem was articulated, and flagged Braille) but if I was re-running this simulation I would give more specific instructions for handling cases like these.

Are the AI’s answers any good?

Based on previous work I’ve done using AI to answer questions about the nature of technology and science, I expected the answers I got to be reasonably good, though not perfect. But while AI hallucinations have become much less of a problem, the ability of an AI model to provide quality output on tasks like this is definitely still a moving target. So I took several steps to try and verify Claude’s answers.

First, I did spot checks for various specific, verifiable claims that Claude made regarding various required predecessors. In the arc lamp example above, for instance, I checked whether Galvani published research on electric current in 1791 (he did). And for 20 of the inventions, I fed the entire output back to Claude, and asked it to pair each verifiable claim with a reliable source, and flag any cases where the claims were incorrect or a reliable source couldn’t be found. (I then checked these sources for accuracy.) This yielded an accuracy rating of around 97% — only a tiny number of verifiable claims were inaccurate, and the inaccuracies were relatively minor (such as a claim that something was common by the 1850s when it should have been the 1860s.)

Checking people and dates is one thing, but this doesn’t tell us the most important part: whether Claude correctly identifies the actual binding constraints for creating a given invention. To try and verify this, I read through Claude’s analysis of several inventions that I know well enough myself.

Here’s the first sample answer, which Claude produced for the Fleming valve:

The Fleming valve is essentially an Edison-effect lamp with the cold electrode brought out as a usable anode and the device used as a rectifier. All physical prerequisites — evacuated bulb, carbon filament, sealed lead-throughs, battery, galvanometer — were in place by 1880. The binding item is the Edison effect itself (1883). If we treat thermionic emission as off-limits before 1883 (it required Edison to notice the unilateral current in his lamps, not something a rectifier-builder would set up to look for), the earliest plausible date is essentially 1883–1890: a motivated team aware of Edison’s observation could have made the small inferential leap to “this rectifies AC” almost immediately, especially once Hertz (1887) created a clear use case for a sensitive RF detector. Earliest straightforward sits in the late 1890s, after Hertzian-wave work motivated multiple labs (Marconi, Lodge, Braun, Fleming) to seek better detectors than the coherer; Fleming himself was investigating the effect from the early 1890s and could have arrived at the diode concept several years before his 1904 patent. The historical 1904 date reflects how long it took to connect Edison’s curiosity finding to the practical problem of wireless detection.

Here’s the second, the Wright brothers’ airplane:

The Wright Flyer is a clean case where the binding constraint is the lightweight internal combustion engine. Cayley had laid out the basic principles of fixed-wing flight by 1810, gliders were practical from mid-19th century, and Wenham’s wind tunnel (1871) plus Lilienthal’s glider work (1890s) gave aerodynamic data. The three-axis control concept is team-discoverable through glider iteration — it’s an engineering insight, not a scientific discovery. The hard prerequisite is a roughly 10-hp engine weighing under ~200 lb, which only becomes feasible after the Otto cycle (1876) and Daimler/Benz refinements of the mid-1880s; steam plants of earlier eras were too heavy. With a Daimler-class engine available by ~1885 and Lilienthal-style glider knowledge accumulating, a motivated team could plausibly assemble a Flyer-equivalent in the late 1880s (earliest plausible). By the late 1890s prerequisites were widespread enough that multiple teams (Langley, Maxim, Ader, the Wrights) were actively converging — making 1895–1903 the straightforward window. The historical 1903 date reflects how genuinely difficult the integrated control problem was, not missing prerequisites.

And here’s the third, for the jet engine:

The turbojet is a system-integration invention rather than a new-phenomenon invention; nothing in it requires post-classical physics. The binding constraint is the maturity of turbomachinery — specifically a compressor and turbine pair efficient enough that the cycle yields net positive thrust, plus turbine blades that survive the combustion-gas temperatures. Parsons’s steam turbine (1884) and the first gas-turbine attempts (Stolze, Armengaud-Lemale, ca. 1900–1906) demonstrate that the basic building blocks existed by the early 20th century, though early gas turbines barely broke even because compressor efficiency was marginal. A motivated team in 1905–1915 with access to Parsons-class turbomachinery, a centrifugal compressor, and contemporary nickel-chromium steels could plausibly run a short-duration jet — Whittle’s 1937 W.U. ran on similar materials. By the mid-1920s, compressor efficiency, high-temperature alloys, and aerodynamic theory had matured enough that multiple teams could converge straightforwardly, which is essentially what happened: Whittle (UK) and von Ohain (Germany) independently produced running turbojets within a few years of each other. The historical lag from “straightforward” to flight reflects funding skepticism and the engineering grind of making it light, reliable, and aircraft-ready, not a missing fundamental.

All three of these answers are pretty close to what I would have given. The Fleming valve is basically gated by the incandescent light bulb. Once the bulb existed, the phenomenon of thermionic emission (then named the Edison Effect) was quickly observed, and could have been exploited soon after by a motivated person. The connection to radio is also correct — Fleming was in fact a consultant for the Marconi Company, and invented the Fleming valve specifically for use in early radios.

For the Wright brothers’ airplane, a lightweight engine was indeed an important gating technology, and needing to wait until Otto’s internal combustion engine got light enough in the 1880s is a reasonable judgment. Thomas Edison worked on the problem of mechanical flight in the 1880s, but judged that what was needed was an engine with a very high power-to-weight ratio, and only made some cursory attempts to build one before giving up. Samuel Langley, one of the Wright brothers’ contemporaries, invested an enormous amount of his aircraft development efforts into building an extremely efficient gasoline engine; the resulting Manly-Balzer engine held the record for power-to-weight ratio for many years. The Wrights’ efforts were notable not because the brothers thought the engine was unimportant, but because they (correctly) thought that by the early 1900s engine technology was advanced enough that obtaining a good-enough engine wouldn’t be overly difficult.

You could argue that the “earliest plausible” date for the airplane could be pushed back somewhat earlier, into the 1870s or so, by someone building a barely-working airplane with a janky, dangerous steam engine. Langley, in fact, built several model aircraft that successfully flew using these sorts of engines. But Claude’s answer here seems defensible. And like Claude, I similarly would have noted that the control problem was extremely difficult, and the solution to it non-obvious, even if there weren’t technical barriers preventing it from being solved with the available contemporary tech.

For the turbojet, Claude is right on both gating technologies: sufficiently efficient compressors, and turbine blade materials that are capable of withstanding high temperatures. And it’s right as to when the technologies started to get good enough: the first gas turbine that could (barely) do actually useful work appeared in the early 1900s, and stainless steels (which Whittle did in fact use for his first turbine blades) started to appear in the 1910s. I probably would have pushed the “earliest plausible” date somewhat later — maybe 1915-1925 — but Claude’s conception of a sort of barely-working jet engine that’s not really good enough to use in an actual airplane a few years early doesn’t seem impossible.

Based on these checks, I think Claude’s answers are probably reasonably defensible most of the time. And what’s more, they show how genuinely difficult it is to answer these sorts of “how early could a technology have appeared?” questions. Even limiting ourselves to questions of technical possibility and not actual commercial or societal usefulness, good answers to these questions require knowing both a lot of history of some particular technology AND having deep technical knowledge of various technologies as they existed at various points in time. Answering “how early could an airplane have been built?” requires knowing not just who the various flight pioneers were and when they did their work, but the state of steam and internal combustion engine technology at various points in the 19th century and how far they could have plausibly been pushed. There are just vanishingly few people with this sort of deep knowledge about even a few technologies, much less 190 of them spread across two centuries. So while I don’t expect the AI’s answers to be perfect, I think it’s probably collectively a much better analysis than you’d get from almost any single human.

The results

So, how long do we have to wait for new inventions? To start, let’s look at a scatterplot of the results from the Claude simulation. The graph below shows how much earlier on average each invention could have appeared, for both the “earliest plausible” and “earliest straightforward” date ranges.

We can clearly see a few trends on this graph. One is that for most inventions, the gap between when it could have been invented and when it was actually invented is not particularly large. Of the 166 inventions Claude estimated a date for, 107 of them (64%) had an “earliest plausible” date 50 years or less from the actual date, and 150 of them (90%) had an “earliest straightforward” date 50 years or less from the actual date. For more than half the inventions, the average earliest straightforward date of invention was 10 years or less from the actual date.

Conversely, there were a relatively small number of inventions where the gap between “could have been invented” and “was invented” was very large. 30 inventions (18%) had an average gap of more than 100 years between “earliest plausible” and actually invented, and eight inventions had a gap of more than 1000 years. You can see this clearly on a histogram, which shows a large bump of small time gaps, and a long tail of fewer, larger gaps.

The inventions with the longest period between “could have been invented” and “was invented” are below.

There’re a few interesting trends observable here. Many of the longest-delayed inventions — the hypodermic needle, general anaesthetic, stethoscope — are medical inventions. (You could argue the surgical mask could be in this category as well). For the hypodermic needle, this probably needed to wait until the existence of some substance that needed to be injected (such as morphine, first synthesized in 1804), but for other medical inventions this possibly also reflects folks’ reluctance to do inventive-tinkering in a medical context. For general anaesthetic, for instance, the trial and error of getting the dose right was incredibly dangerous, and the inventor Hanaoka Seishu “crippled his mother and blinded his wife perfecting the dose.”

Several of the longest-awaited inventions are ones where the version in the list is an early, impractical version of the one that actually solved a problem. So the “dandy horse” — a two-wheeled, wooden vehicle that was a predecessor of the bicycle — could have been built in antiquity, but the dandy horse wasn’t particularly practical as a means of transportation, and actually useful bicycles had to wait for the improved manufacturing technology of the later 19th century. Likewise, the version of the ballpoint pen that Claude thinks could have been invented much earlier is John Loud’s 1888 version, but Loud’s pen worked poorly and wasn’t successful. Actually useful ballpoint pens are surprisingly difficult to manufacture (China famously couldn’t manufacture them until very recently), and credit for the “useful ballpoint pen” is usually given to Lazlo Biro in 1938. (Claude correctly notes that “useful” versions of both these inventions would need to wait until much later.) Judson’s early zipper and de Martinsville’s early sound-recording device are also examples of early, not-particularly-useful inventions.

Other inventions on this list seem like they might be a case of the surrounding social or technological conditions needing to be right for the invention to appear. So Otis’ elevator safety brake needed to wait until elevators were in higher demand, which probably didn’t occur until steam engines or some other similar power source came along (though maybe you could have water-driven elevators much earlier). Barbed wire perhaps needed to wait until enclosing very large areas of land for grazing became something people needed to do.

And some inventions seem like they might have been genuinely useful had someone thought of them earlier, and simply nobody did. Blanchard’s pattern-tracing lathe, Neilson’s hot blast, and the safety pin all seem like they fall into this category, though perhaps there were good reasons these didn’t appear earlier.

Going back to the scatterplot, the other obvious trend on this chart is that the gap between when an invention becomes possible and when it appears has narrowed over time. If we graph the average and median gaps for inventions by 20-year time periods, we can see that they have fallen over time.

For the 60 post-1900 inventions, every one has a “straightforward” invention date of 50 years or less than the actual date, and 75% of them have a straightforward date of 10 years or less before the actual date. Of the 30 inventions with a gap of more than 100 years between when they could have been invented and when they actually appeared, 29 of them were invented before 1900. So the process for creating new inventions seems to be getting more and more efficient — opportunities are getting noticed and exploited sooner and sooner, up through 1970 at least (which is when the list of major inventions extends to).

We can also look at how wait times vary by type of technology. The chart below shows average wait times by different categories, for both inventions overall and for just post-1900 inventions. We can see that medical inventions have the longest wait, while electronic inventions have the shortest wait.

We can also look at what types of factors tend to be bottlenecks. For some inventions, the bottleneck is primarily scientific: the limiting factor for the transistor is the band theory of quantum mechanics, and the limiting factor for the radio was Hertz’s demonstration of electromagnetic waves. But for other inventions, it’s primarily technological: the turbojet had to wait not for some new physical theory, but until compressor technology and high-temperature steels appeared; likewise the airplane had to wait not for some novel theory of aerodynamics but until a light enough engine appeared. The chart below shows how often “science” or “technology” was the limiting factor for a given invention, for both inventions overall and post-1900 inventions.

In both cases, technology is the bottleneck far more often than science (though of course if you removed enough technological bottlenecks eventually you’d hit a scientific one, and vice versa).

Conclusion

There is of course only so much you can learn from this sort of exercise: at the end of the day, this is based on an AI’s best guess, not a thorough analysis of the various controlling factors by experts. But while I wouldn’t swear to its accuracy, I think the answers are probably mostly pretty good, and enough for us to draw some general (if tentative) conclusions about the nature of technological progress.

My main takeaway is that we mostly don’t wait all that long for new inventions. Since 1800 most inventions have appeared within a few decades of when it was possible to build them, and since 1900 these gaps been even narrower. It also seems likely that medical inventions are more likely to have long wait times than other types of inventions, and that the limiting factor for how early some new technology could appear is most likely to be technological, rather than scientific.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week we look at chilling effects in the build-to-rent sector, how fast could robot manufacturing scale up, PJM’s new interconnection queue, the backlash against battery storage, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

War in Iran

The latest war-related supply chain disruption? Printed circuit boards. “Iran struck Saudi Arabia’s Jubail petrochemical complex in early April, ​forcing a halt in production of high-purity polyphenylene ether (PPE) resin — a critical base material used to ⁠manufacture PCB laminates…In April alone, PCB prices surged as much as 40% from March, Goldman Sachs ​analysts said in a recent note.” [Reuters]

Thanks to high jet fuel prices, India’s airlines are apparently “on the verge of closing down.” [SCMP] And the biggest US airlines collectively spent $1.2 billion more on jet fuel in the first quarter of 2026 compared to 2025. [Sherwood]

Shipping isn’t the only sort of Strait of Hormuz traffic that the war has the potential to disrupt. The strait also carries a large number of undersea cables that could possibly be damaged if damaged ships drag their anchors across them. ““In a situation of active military operations, the risk of unintentional damage increases, and the longer this conflict lasts, the higher the likelihood of unintentional damage,” Kotkin said. A similar incident occurred in 2024, when a commercial vessel attacked by Iran-aligned Houthis drifted in the Red Sea and severed cables with its anchor.” [Reuters]

Housing

One problem with ill-considered legislation is that negative consequences can sometimes be caused by the mere possibility that it passes. So if you have a housing bill with a provision that would make it much more difficult to build homes for rent (by requiring institutional owners to divest those homes after seven years), even the mere threat of it passing is enough to slow down rental housing construction. “Developer TerraLane Communities was about to start construction on two new housing communities in Arizona and Texas, projects that would create around 300 new single-family homes to rent out. But before any shovels got in the ground, the Senate passed a bill that severely restricts the build-to-rent business. Uncertain about the industry’s legislative future, investors demanded that TerraLane pause the project. The firm had five other potential build-to-rent deals that it is no longer pursuing.” [WSJ]

When is the best time to buy and sell a home? The graphic below tracks the conventional wisdom – better to buy in the summer when there’s more listings, and sell in the winter when there’s fewer — but it’s interesting to see the data. [Substack]

We previously looked at UK building standards which, if complied with, effectively require houses to have extremely small windows so they can be cleaned by an elderly woman without stretching. The Wall Street Journal takes a closer look at this requirement. [WSJ]

Americans who are importing their homebuilding materials from China. [CNN]

So far this year Idaho has passed six bills to make it easier to build housing. [Sightline]

Manufacturing

The data center buildout continues to put pressure on every related area of the supply chain for them. To meet the rising demand for AI chips, ASML is ramping up production of EUV machines. [WSJ]

China is no longer the sole producer of a critical mineral, samarium, used to make military-grade magnets. [WSJ]

AI-analysis nonprofit Epoch AI asks how quickly robot manufacturing could scale up. “Historical demand shocks such as WWII mobilization, or the production of drones following the war in Ukraine have accelerated production growth rates by 1.4–2.2x in the most comparable cases. This would imply doubling times of roughly 5–8 months for quadrupeds and 10–16 months for mature form factors. It’s unclear whether these cases are optimistic or pessimistic for robotics.” [Epoch]

The US’s recent…unreliability with regards to its foreign allies has apparently inspired European countries to take defense production more seriously. Germany has ramped up ammunition manufacturing, and is now producing more ammunition than the US. [Newsweek]

On the other hand, the US is apparently seriously considering using foreign shipyards for naval ship design and component manufacturing. [USNI News]

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Though wind and solar continue to carve out larger and larger shares of world energy supply, the modern world still runs on petroleum, and will continue to do so for the foreseeable future. The world consumes over 100 million barrels of oil a day. As of 2023, oil was responsible for 30% of all energy use worldwide, higher than any other energy source (though its share has been gradually falling). In chemical manufacturing, petroleum is even more critical: an astounding 90% of chemical feedstocks are derived from oil or gas. Virtually all plastic comes from chemicals extracted from oil or gas, and petrochemicals are used to produce everything from lubricants to paint to plywood to synthetic fabrics to fertilizer.

Our enormous consumption of petroleum is made possible by oil refineries. When oil comes out of the ground, it’s a complex mixture of thousands of different chemicals. Oil refineries take in this mixture and process it, turning it into chemicals we can actually use. Because of the scale of worldwide petroleum consumption, oil refineries are some of the largest industrial facilities in the world. A large oil refinery will occupy thousands of acres and cost billions of dollars to construct, ultimately refining hundreds of thousands of barrels of oil each day.

Crude oil basics

Oil is a liquid produced from decomposing organic materials, mostly plankton and algae that died and sank to the bottom of ancient oceans. This dead organic matter gradually got covered with sediment, and over millions of years it transformed into crude oil. Crude oil is a mixture of thousands of different chemicals, most of which are hydrocarbons: molecules that are various arrangements of carbon and hydrogen atoms. The molecules in crude oil range from the simple, such as propane (three carbons and eight hydrogens) and butane (four carbons and ten hydrogens) to the complex — some asphaltene molecules in crude oil can contain thousands of individual atoms.1

Crude oils extracted from different parts of the Earth will have different mixtures of hydrocarbons and other molecules, which has given rise to a sort of crude oil taxonomy. “Heavy” crude oils, found in places like Canada’s oil sands, will have more heavy molecules, while “light” crude oils found in places like Saudi Arabia’s Ghawar field will have more light molecules. “Sweet” crudes, like the crudes extracted from the Brent oil field in the North Sea, have lower sulfur content, while “sour crudes,” like some of the crudes extracted from the Gulf of Mexico, have greater sulfur content.

The job of an oil refinery is to process this mixture of hydrocarbons and other molecules: separating the mixture into individual chemicals or groups of chemicals, and using various chemical reactions to change low-value chemicals into more valuable, useful ones.

Refinery basics

A refinery makes use of several different methods to separate and process crude oil, but the most important process of all is probably distilling. Different molecules within crude oil boil at different temperatures, and condense back into liquid at different temperatures. Smaller, lighter molecules boil and condense at lower temperatures, while larger and heavier molecules boil and condense at higher temperatures. You can describe this range of boiling points with a distillation curve, which shows what fraction of the crude oil boils at different temperatures. In the example curve below, we can see that at about 350°C half the crude has boiled off, and at 525°C about 80% of the crude has boiled off. Different crude oils will have slightly different distillation curves, depending on the proportion of different molecules within them.

Substances derived from crude oil are often mixtures of chemicals defined by their range of boiling points. Gasoline, for instance, isn’t just one chemical: it’s a mixture of hydrocarbons, mostly molecules with between four and 12 carbon atoms. The EIA defines finished gasoline as “having a boiling range of 122 to 158 degrees Fahrenheit at the 10 percent recovery point to 365 to 374 degrees Fahrenheit at the 90 percent recovery point.”2

Oil refineries can use this range of boiling and condensation to separate crude oil into different groups of chemicals, or fractions, using a distillation column. When crude oil enters a refinery, the salt gets removed from it, and it’s then heated to around 650-750°F, which turns most of the oil into a vapor. The vapor is then fed into a tall column containing trays at different heights, each filled with liquid. As the hot vapor rises through the column, at each tray it passes through the liquid, which cools it slightly. When the vapor cools enough, it condenses back into liquid. The heaviest molecules with the highest boiling points condense first, at the bottom of the column, while the lighter ones condense last, at the top. The very lightest molecules don’t condense at all: they exit the top of the column while remaining a gas. At the same time, the very heaviest molecules remain a liquid the entire time, and exit the bottom of the column. Thus, different molecules of different weights can be separated out.

Essentially every oil refinery first distills crude oil into various fractions in a distillation column, though the exact fractions separated might vary from refinery to refinery. Because this distillation is done at atmospheric pressure, this first step in the refining process is referred to as “atmospheric distillation.” The simplest refineries might only do atmospheric distillation, but most refineries will then send these various fractions along for further processing. There are a LOT of processes that a refinery might use, depending on what it’s designed to produce, so we’ll just look at some of the most widely used ones.

The gas that comes out of the top of atmospheric distillation will be a mixture of several different light molecules — propane, methane, butane, isobutane (butane with a slightly different molecular arrangement) and so on. To separate this mixture into its component gases, a refinery can send it to a gas plant, which contains a series of distillation columns designed to condense various substances out of the mixture. So gas might flow through a “debutanizing tower” to separate butane, propane and lighter gasses from the rest of the mixture; the butane-and-lighter gasses might then be sent to a “depropanizing tower” to separate the propane from the butane.3

While light gases come out of the top of a distillation column, heavy liquids come out the bottom. The very heaviest molecules, which emerge from distillation without ever having evaporated at all, are known as residuals. Many of the heavier molecules aren’t particularly valuable by themselves, and thus one of the most important functions of a refinery is cracking — splitting heavy fractions, such as heavy fuel oil, into lighter, more valuable ones such as gasoline.

Cracking was invented in the early 20th century as a way to extract more gasoline from a barrel of crude oil to meet rising demand from car usage. Over the years cracking methods have evolved, and today most refineries use some flavor of catalytic cracking (or “cat cracking”). In catalytic cracking, the heavy fractions from atmospheric distillation are mixed with a catalyst (a material designed to speed up chemical reactions) and subjected to heat and pressure, splitting the heavy molecules into lighter ones. The catalyst is then separated from the mixture using a cyclonic separator — essentially, the mixture is spun around, separating out the heavier catalyst from the rest of the mixture — cleaned, and reused, while the now-cracked (and therefore vapor-izable) oil is sent to another distillation column which splits it into various fractions.

Most catalytic cracking is fluid catalytic cracking, which uses a sand-like catalyst that behaves as a fluid when mixed with the heavy fractions. Different companies have developed different fluid catalytic cracking processes, and different refineries might use multiple catalytic crackers in different parts of the process.

Catalytic crackers are designed to encourage the chemical reactions that break apart heavy hydrocarbons, but these reactions can also occur within the distillation column if the heat is high enough. Because cracking is disruptive to the distillation process, refineries limit the temperature in atmospheric distillation to around 650-750°F. This leaves behind a mixture of heavy, unboiled hydrocarbons at the bottom of the column. It would be useful to further separate this mixture into different fractions so that it could be reclaimed, but atmospheric distillation can’t do that without raising the temperature to the point where cracking starts to occur.

The solution is to send this mixture to another distillation column that’s kept at very low pressure, near vacuum — this process is thus known as vacuum distillation or vacuum flashing. Lower pressure means lower boiling points, allowing the heavy fractions to be distilled without heating them to the point where cracking starts to occur.

Some of the heavy fractions that come out of vacuum distillation might be sent directly to a catalytic cracking unit to split them into lighter ones. But the very heaviest molecules that come out of the bottom of the vacuum distillation column aren’t suitable for catalytic cracking — many of them contain heavy metals that would poison the catalyst, and the chemical reactions of these molecules tend to produce coke (a carbon-rich solid), which would gum up the catalyst. Because it’s useful to crack these very heavy molecules, some refineries will use thermal cracking processes, which use heat to split molecules apart. Cokers are thermal crackers that use heat to crack the heaviest molecules into lighter ones and coke. The lighter molecules are sent to a distillation column to be separated; the coke can be burned as fuel, or as a manufacturing input (the electrodes used in aluminum smelting, for instance, are made from coke). Another type of thermal cracking, visbreaking (short for viscosity breaking), is used to crack some molecules and reduce the viscosity of the remaining fractions.

Besides cracking, a refinery might employ a variety of other processes to modify the chemical structure of various molecules. Catalytic reforming takes the naphtha fraction (the part of the crude oil with a boiling point between ~122°F and ~400°F) and exposes it to heat and pressure in the presence of a catalyst to produce a new mixture of chemicals called reformate that is used to make gasoline. Isomerization processes take various molecules, such as butane, and modify their physical arrangement to produce isomers – molecules with identical chemical formulas but different structural arrangements. Hydrotreating reacts various crude oil fractions with hydrogen in the presence of a catalyst to remove impurities and improve their quality. (Hydrotreating can be done on its own, but it’s also often combined with other processes. Hydrocracking combines hydrotreating with catalytic cracking, and residue hydroconversion combines hydrotreating with thermal cracking.)

To store the various inputs and outputs of these processes, oil refineries also have huge numbers of storage tanks called tank farms, which are capable of storing millions of gallons of various liquids. Gases like propane and butane will typically be stored as pressurized liquids, either in above-ground tanks or in underground caverns or salt domes.

Chevron’s Richmond refinery

To get a sense of how these various processes might be arranged, we can look at how they’re implemented in an actual refinery. The map below shows Chevron’s Richmond, California refinery, a moderately large refinery capable of processing about a quarter million barrels of crude oil a day. The tank farm occupies the south half of the site, while the processing area wraps around the north and east.

The chart below shows the daily capacity of various processes at the refinery.

We can see that Chevron Richmond has many of the processes that we described above: in addition to ~257,000 barrels of atmospheric distillation, it has ~123,000 barrels of vacuum distillation, ~90,000 barrels of catalytic cracking, and ~71,000 barrels of catalytic reforming. (Chevron Richmond doesn’t have any coking capacity, but Chevron’s slightly larger El Segundo refinery in Los Angeles does.)

To see how these processes are actually arranged, we can look at a process flow diagram for the refinery. (This diagram is available because several years ago Chevron extensively modified this refinery, which required them to submit a very detailed environmental impact report to comply with California’s environmental quality laws.)

We can see that the refining process starts with atmospheric distillation (though the refinery also processes some heavy gas oil that can skip the distillation process), which separates the crude into various fractions. These fractions then get routed to various other processes. The light gas gets sent to the gas plant, while the naphtha gets sent to hydrotreating, catalytic reforming, and isomerization. Jet fuel and diesel fuel are sent to their own hydrotreating processes, and the heavier fractions get sent to various catalytic cracking processes. The output of all these processes is various crude oil products: heavy fuel oil, diesel, jet fuel, lubricants, and, of course, gasoline.

A broader look at oil refining capacity

Chevron Richmond is just one of 132 operable oil refineries in the U.S., which collectively can refine over 18 million barrels of crude oil each day. The location of these refineries is highly concentrated: most of them are on the Gulf Coast of Texas and Louisiana, with other clusters in New Jersey, the Midwest, and in California.

If we look at the distribution of refinery capacity we can see that Chevron Richmond is on the larger side, but far from the largest. Around a fifth of US refineries are roughly as large or larger than Chevron Richmond. Six US refineries are more than twice as large, with the capacity to refine more than half a million barrels a day. And some refineries around the world are even bigger: the Jamnagar refinery in India, the world’s largest refinery by raw capacity, can refine 1.4 million barrels of crude per day.

But looking at capacity in barrels per day (which is essentially atmospheric distillation capacity) only tells part of the story. As we noted, different refineries will have different processing equipment installed depending on what they’re designed to produce. Simple refineries will have little more than atmospheric distillation, while more complex ones will employ long sequences of processes to produce a wide range of highly refined products. The chart below shows the collective US refining capacity of various processes.

We can look at the relative complexity of different US refineries using the Nelson Complexity Index, which is intended to measure how complex a refinery is. The index is constructed by taking each process a refinery employs, and multiplying its refining capacity by a “complexity factor” that compares the cost of that process to atmospheric distillation, and then dividing by the refinery’s atmospheric distillation capacity. So a refinery that has 100,000 barrels of atmospheric distillation capacity (complexity factor of 1) and 50,000 barrels of vacuum distillation capacity (complexity factor of 2) would have a Complexity Index of 1 + 2 * 50,000 / 100,000 = 2. If it then added 25,000 barrels of catalytic cracking capacity (complexity factor of 6), its Complexity Index would rise to 1 + 1 + 6 * 25,000 / 100,000 = 3.5.

Most refineries in the US are fairly complex. As of 2014, less than 3% of refineries had a complexity index of 2 or less, and the average complexity index was 8.7. As of 2014 the Chevron Richmond refinery had a complexity index of 14, above average for US refineries. The Jamnagar refinery, in addition to being the world’s largest, is also particularly complex: its complexity index of 21 would make it more complex than virtually any US refinery.

Conclusion

What strikes me most about oil refining isn’t the complexity of the process — indeed, while the arrangements of various processes are often exceedingly complex, many of the processes themselves are often surprisingly simple (conceptually, at least). What strikes me is the sheer scale of it. Refining is an expensive undertaking not necessarily because the processes are so complex, but because the volume of material that has to be processed is so high. Chevron’s Richmond refinery is the size of a small city, and can process the entire contents of a Very Large Crude Carrier in a little over a week. And Richmond isn’t even a particularly large refinery: the US has 25 refineries that size or larger, and six refineries that are more than twice as large. Worldwide, it takes 400 Richmond-size refineries to keep the world fed with petroleum.

If you live in Texas or Louisiana these aspects are probably obvious to you, but most of us are able to go about our lives without ever thinking about the huge industrial machine that keeps the blood of civilization flowing. But the US consumes over 20 million barrels of oil a day, every day, and it takes a vast complex of oil refineries to make that possible.

Welcome to the reading list, a list of news and links related to buildings, infrastructure, and industrial technology. This week we look at transformer steel manufacturing, textile engineering, bringing power plants online quickly, infrasound, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

War in Iran

This week in Strait of Hormuz supply chain issues: a shortage of battery ingredients [Heatmap], the world’s top condom producer plans to raise prices by 20-30% due to petrochemical supply disruption [X], and Lufthansa plans to cut 20,000 flights due to rising jet fuel costs. [UPI]

And it seems like the closure might not be resolved any time soon. It could apparently take up to 6 months to clear the Strait of Hormuz of mines. [Washington Post]

Housing

The Economist on SB-79, and other efforts to stimulate the production of housing in California. “SB 79 rezones state land around busy public-transport stops to allow for taller residential buildings. It also slaps hefty fines on cities that try to deny such buildings a permit. It was amended more than a dozen times to appease rural lawmakers, unions and tenants-rights groups—and it still barely passed the legislature. The bill spent weeks on the governor’s desk, which gave his pro-housing allies the willies and Mr Pratt some hope. But on October 10th Mr Newsom signed the law and delivered a huge win to the ascendant YIMBY (Yes In My Backyard) movement. The passage of SB 79 and more than 40 other housing reforms this year could be a turning point for a state that is crippled by its self-inflicted housing shortage.” [Economist]

Manufacturing

A good Substack post on transformers and why their various components – such as grain oriented electrical steel — are difficult to make. “Producing [GOES] is one of the most demanding metallurgical processes in heavy industry. The slab has to be reheated above 1,350 degrees Celsius to dissolve precipitate inhibitors that later pin grain boundaries, then cold-rolled to the final gauge, and finally decarburized in wet hydrogen to bring the carbon content below 0.003 percent. The entire coil goes into a high-temperature box anneal at 1,200 degrees Celsius for five to seven days, and during that week inside the furnace, through a phenomenon called abnormal grain growth, Goss-oriented grains consume the rest of the matrix and grow to centimeters across a sheet that is barely thicker than a credit card. Premium grades are then laser-scribed in transverse lines to refine the magnetic domains and cut losses by a further ten to fifteen percent. Each step is unforgiving, and a single deviation in composition, atmosphere, or timing ruins the whole coil.” [Frontier Map]

Also on the subject of steel, Did low-quality steel contribute to the sinking of the Titanic? “The steel used in constructing the RMS Titanic was probably the best plain carbon ship plate available in the period of 1909 to 1911, but it would not be acceptable at the present time for any construction purposes and particularly not for ship construction.” [TMS]

Greg Ip of the WSJ claims that the US is in a stealth manufacturing boom, though not one that’s creating jobs. “Since January 2025, manufacturing jobs have indeed fallen by about 100,000 workers, or roughly 0.6%. In the same period, though, manufacturing production rose 2.3%, and manufacturing shipments, unadjusted for inflation, climbed 4.2%.” [WSJ] But Noah Smith argues that no, we aren’t — most manufacturing indicators, once adjusted for inflation, show little to no growth. [Noahpinion]

Chinese electric car manufacturer BYD wants to open 20 dealerships in Canada this year. [GM Authority]

TSMC is apparently delaying using the latest and greatest ASML EUV machines — their High NA machines — because they’re too expensive. [Bloomberg] It’s not clear to me if this is a chance for Intel (who has purchased several High NA machines) to pull ahead, or if Intel overcorrected and made a bad call adopting them.

The changing ownership and subsequent evolution of two different US tool brands, Milwaukee and Craftsman. [Worse on Purpose]

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Understanding trends in productivity — how much output we get for a given amount of input over time — is useful, but it’s also useful to look at other metrics of construction industry progress. One particularly salient measure is construction costs: how much money it takes to build a house or an office or an apartment building, and how those costs have changed over time. Cost is a good improvement metric because it directly tracks what we actually care about: we would like the costs of building housing, buildings, and infrastructure to fall and become more affordable, and we basically care about more abstract measures like productivity to the extent that they’re a proxy for costs.

Unsurprisingly, when we look at construction costs we see similar trends to what we saw with construction productivity; construction rarely gets any cheaper over time, and construction costs tend to rise at or above the level of overall inflation. As with productivity, we see this when we analyze the data at different levels of granularity, and we see it in both the U.S. and in countries around the world.

Construction cost indexes

Changes in construction cost are generally tracked using cost indexes, measures produced by various organizations which collect and analyze data to try and capture large-scale changes in construction cost. At a high level, there are two broad types of index: output indexes, and input indexes. Output indexes try to measure changes in the cost of finished buildings or infrastructure: how much it costs to build a house, or an office building, or a segment of road over time. Input indexes measure changes in the cost of some basket of construction inputs: the price of different construction tasks, or materials, or labor.

It’s not always straightforward to tell whether an index is an output index or an input index, because exactly how indexes are constructed can be somewhat opaque. An index that initially appears as if it’s an output index, because it apparently tracks changes in a particular type of construction (like new apartment buildings), may actually function more like an input index if it is constructed from price changes in inputs specific to that type of construction. All else being equal, I prefer output indexes to input indexes, because they should more closely track what we actually care about (the cost of finished buildings), and should be less subject to distortion. For instance, the invention of some great cost-saving construction method might not be reflected in an input index that simply tallies up the cost of 10 hours of labor, 100 pounds of steel, and 1 ton of cement (which is how many input indexes are constructed). But in practice output and input indexes tend to track each other quite closely.

Cost indexes are resistant to some of the measurement difficulties that dog productivity metrics, because they’re typically constructed to try and mirror the cost changes of actual buildings. For instance, we’ve previously seen that productivity metrics are dogged by problems of “changes in the output mix” — changes in the type of construction that takes place in a given geography or during a particular collection period can mask actual productivity trends. But the producers of cost indexes will often monitor trends in the construction marketplace, and modify how their index is constructed by weighing some items more heavily and other items less heavily to try and reflect that. We should thus expect them to be more resilient to changing output mix problems.

But in some cases cost indexes share the same measurement issues as productivity metrics. In particular, it can be difficult to adjust cost indexes for quality; a modern building might cost more per square foot, but be built to higher standards or otherwise have higher performance than an older building, which looking only at changes in costs won’t capture. Some indexes, such as the Census Bureau’s Constant Quality Index, try to account for quality changes, but most don’t. (This is in contrast to, say, the Bureau of Labor Statistics’s sector-specific inflation measures, which try to take into account quality changes when calculating inflation trends for things like TVs or new cars.) Indexes that do try to adjust for quality changes likely can’t account for it completely. These issues are somewhat mitigated by the fact that we care about costs as such, and it’s valuable to know how those costs are changing — i.e., even if some proportion of rising costs is due to increased standards and we are getting more bang for our buck, it’s still useful to know how construction costs are changing with respect to other prices. Nonetheless, we should keep this point about quality changes not always being reliably captured mind when we’re looking at cost trends.

To look at trends in U.S. construction costs, we’ll use the following indexes:

Output indexes

• The Turner Building Cost Index — Produced by Turner Construction, one of the largest general contractors in the US, this index tracks the price of non-residential buildings by considering such factors as “labor rates and productivity, material prices, and the competitive condition of the marketplace.” This is one of the oldest continuously produced construction cost indexes, going all the way back to 1915.

• The Census Bureau’s Single-Family Constant Quality Index — Produced by the US Census Bureau, this index tracks changes in the price of single-family homes, and goes back to 1964.

• Handy-Whitman — Produced by Whitman Requardt and Associates, data in this index tracks the cost of building reinforced concrete, brick-lined utility buildings (though there are also other data for other types of buildings). The index is constructed by looking at the price of various inputs (materials, labor, equipment) for these types of buildings, but the relative proportions are adjusted to ensure that they reflect “current construction practice,” so I’m classifying this as an output index. I was able to get data for this index from 1915 to 2002.

• Craftsman single-family home costs — Craftsman’s National Construction Estimator, an estimating guide that has been published since the 1950s, includes an estimated cost per square foot to build a “typical” single-family home in the U.S. I was able to get these values going back to 1966.

• The National Highway Construction Cost Index — Produced by the U.S. Federal Highway Association, this index tracks the cost of building highways over time, and is based on the price of winning bids for highway construction contracts. This public index goes back to 1915.

Input indexes

• E.H. Boeckh Index — Produced by E.H. Boeckh and Associates, this index tracks the cost of a variety of different building types in cities around the U.S., based on “115 elements,” including labor costs, material costs, and tax and insurance elements. (I’m including this in the input indexes because I think it’s basically using the basket-of-inputs approach to construct costs, but depending on how they weighed these elements this might make more sense as an output index.) For many years this index was included in the Survey of Current Business produced by the U.S. Bureau of Economic Analysis. I look at this index for residential construction, for the years 1910 through 1991.

• ENR Construction Cost Index — Produced by Engineering News-Record, this index tracks a basket of several different construction inputs — unskilled labor, steel, cement, and wood — the relative proportions of which are periodically adjusted. ENR also produces a virtually identical “Building Cost Index” that replaces unskilled labor with skilled labor. This index has been continuously produced since 1908.

• RS Means Historical Cost Index — Produced by the RSMeans estimating company, this index tracks a basket of construction labor, materials and equipment costs. I was able to get data for this index going back to 1953.

• Riggleman Index — Produced for an unpublished doctoral dissertation (by Dr. John R. Riggleman) in 1934, this index was made using several other indexes, such as the ENR construction cost index and the American Appraisal Company’s cost index for industrial buildings. This index is primarily useful because it goes back all the way to 1868.

• Blank Residential Index — This is another composite index, which uses a weighted basket of construction inputs as well as the E.H. Boeckh index, to track the cost of residential construction. This index is useful because it goes back to 1889.

We’ll compare each of these indexes to the Consumer Price Index (CPI), a common measure of overall inflation. Because the Consumer Price Index only goes back to 1913, for earlier values we’ll use inflation conversion factors produced by Robert Sahr of Oregon State.

The graphs below show various cost indexes between 1870 and 1950.

And these graphs show cost indexes from 1950 to 2022.

We can see that regardless of time period, and regardless of whether we’re looking at input indexes or output indexes, construction costs are rising roughly as fast as, or faster than, overall inflation. When we looked at productivity trends, we saw that since roughly the 1960s U.S. construction productivity has been stagnant or declining. Cost data suggests that the problem extends even further back, and that U.S. construction costs have virtually never fallen with respect to overall inflation.

These graphs give us a good large-scale view of cost trends for different indices, but it makes it hard to see cost trends over specific time periods. So let’s look at the average annual growth rate for each index over 10-year periods, minus the average growth rate of CPI for the same period. This will let us see how construction costs are changing with respect to inflation over specific periods: positive values mean construction costs are rising faster than inflation, negative means construction costs are rising slower than inflation.

We see that in almost every period of time, construction costs are rising faster than overall inflation for virtually every cost index. The major exception is the period from 1975 to 1995, where most indexes show lower rates of increase or even declines against overall inflation. We also see that historic rates of cost increase seem to be as bad or worse than modern ones. For four of the five 10-year periods between 1915 and 1965, the Turner Cost index rose more than a percentage point faster than overall inflation, whereas for the periods from 1995 to 2025 it rose less than a percentage point.

Construction task costs

As with construction productivity, we can also look at more granular construction cost trends, by looking at how the costs of individual construction tasks have changed. We can do this using construction estimating guides, which provide estimates for the costs of various construction materials and tasks. By looking at the costs of the same, or similar tasks across various versions of estimating guides, we can see how the cost of those tasks are changing.

The chart below shows the cost of 40 different construction tasks taken from three different versions of the RSMeans estimating guide published in 1954, 1985, and 2023.

And this chart shows the cost of 20 different construction tasks taken from several different versions of the Craftsman National Construction Estimator published between 1967 and 2016.

We can see that cost changes in individual construction tasks aren’t uniform. Some have risen in cost faster than overall inflation; others more slowly. But on average, the cost of these construction tasks has risen at the level of overall inflation. So not only are buildings not getting any cheaper to produce on average, the cost of individual construction tasks isn’t falling either on average, at least for this collection of construction tasks.

There are issues with looking at changes in individual construction tasks. As we noted when we looked at construction productivity, all else being equal we might expect construction to improve by way of introducing new, improved processes, and thus looking at changes in older processes might not reveal very much. In the 19th century, nails got cheaper due to the introduction of new nailmaking processes - replacing hand-made nails with the cut nail process, and then the wire-nail process. If we looked only at improvements in hand-made nails, we might conclude that nails on the market hadn’t gotten any cheaper, even though what actually happened was that an older process had simply been replaced by a newer, better process. I’ve tried to avoid this by using construction tasks that I know are still in use, but this isn’t perfect. Unfortunately, this method may run into an adverse selection problem: picking tasks that appear in many versions of the estimating guide might deliberately select for ones that have been difficult to substitute. Nonetheless, it’s the best method we have for analyzing costs at the granular task level.

We can address this issue the same way we did when we looked at construction productivity, by looking at cost trends in broad categories of tasks. The chart below shows the cost per square foot for 32 categories of tasks required to build a single-family home from Craftsman’s National Construction Estimator. As we can see, task costs generally rise at roughly the same rate that overall home prices rise, and rarely change. (This is sort of mechanical outcome of the fact that task category prices are given as a percentage of overall costs, and for most task categories that percentage has changed little over time, but it’s nevertheless notable.)

Thus, at the level of construction tasks, we also see costs tending to rise at or above the level of overall inflation.

International construction costs

To see whether this trend is also observed internationally, we can look at similar construction cost indexes constructed for other countries. The cost indexes we’ll look at are below:

• Eurostat Construction Producer Price Index for Residential Buildings — This cost index, produced by Eurostat for 36 different European countries, tracks the cost changes for residential buildings. (It’s not particularly clear to me how this was constructed: the website merely says that producer price indexes track “the average price development of all goods and related services resulting from that activity.”) For most countries this index goes back to 2000, but for some it goes all the way back to the 1950s.

• The U.K.’s BIS construction output price index — This index tracks output prices for several different UK construction sectors (I used values from “All Construction”), going back to 1955. Because this index only goes up to 2011, I supplemented this with the similar Construction Output Price Index from the U.K.’s Office for National Statistics, which began in 2014.

• Belgium’s ABEX index — This index tracks the price of building residences in Belgium. The Eurostat Producer Price Index includes data for Belgium, but it only goes back to 2000, whereas the ABEX index goes all the way back to 1914(!).

• Japan’s Construction Cost Deflator — This index tracks price changes for several different sectors of Japanese construction, going back to 1960. I used the value for residential construction.

• South Korea’s Construction Cost Index — This index tracks the change in construction costs for several different Korean construction sectors, going back to 2000. I used the index for housing construction.

• Hong Kong’s Building Works Tender Price Index — Tracks the cost of new buildings in Hong Kong, based on contractor bids. Goes back to 1970.

• Taiwan’s Construction Price Index — This index tracks the changes in construction costs in Taiwan, and is based on the prices of 115 different construction inputs. It goes back to 1991.

The graphs below show each of these indexes against the consumer price index for 12 major European countries, as well as the U.S. consumer price index.

And this graph shows construction cost trends for Asian countries.

We see the same pattern that we saw with U.S. construction cost indexes: construction costs nearly always rise at, or faster than, the level of overall inflation in the country.

We can also see this if we look at changes in construction cost minus changes in consumer price index in 10-year buckets, as we did for U.S. construction costs. The chart below shows decade-by-decade changes in construction cost minus CPI for 41 different European and Asian countries (including lots of smaller and poorer countries that I didn’t include on the above graphs).

There’s somewhat more blue on this chart than in the US construction costs, but we can still see costs are, more often than not, rising faster than overall inflation.

Conclusion

When we looked at trends in construction productivity — how much construction output we get for a given amount of input — we saw that it’s mostly either relatively unchanging, or declining over time. We saw this in the U.S. using a variety of different metrics of varying granularity, and we saw it in most other wealthy countries. With construction costs — how much construction output we get for a given amount of currency — we see something similar. Construction costs tend to rise at, or above, the level of overall inflation, and it rarely (if ever) gets cheaper to build houses, offices, or other buildings. We see this in the U.S. with a variety of different metrics, and we see it in countries around the world. With stagnant construction productivity, we could date the problem as far back as roughly the 1960s. With construction costs, we can push the problem back even further: outside of a few windows of time, construction costs have virtually never fallen with respect to overall inflation.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week we look at a quadruped welding robot, the China Shock 2.0, transformer startups, China’s mysteriously moving satellites, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

No essay this week, but working on a more involved piece about construction costs in the US and around the world that should be out next week.

War in Iran

The US has blockaded the Strait of Hormuz, preventing Iranian ships from transiting the strait. “On Monday, the United States imposed its own naval blockade, intent on ending Iran’s dominance of the waterway and cutting off its oil income by blocking all traffic to and from its ports…Since the U.S. blockade took effect, no ships linked to Iran have been spotted leaving the region, according to the vessel‑tracking company Kpler.” [NYT] Negotiations between the US and Iran are apparently ongoing, but the strait seems to be closed as of this writing. [BBC]

The strait’s closure continues to disrupt supply chains around the world: Russia has imposed export controls on helium [Reuters], airlines continue to be squeezed by the high cost of jet fuel [WSJ], and a Japanese bathroom manufacturer shut down production due to a lack of glue. [Nikkei Asia]

Thanks to the war, GPS signals are being jammed across the region. One consequence? Food delivery drivers are having trouble delivering their orders. [Rest of World]

The Saudi East-West oil pipeline being used to bypass the Strait of Hormuz had been damaged by an Iranian drone attack, but now appears to be back online. [Reuters]

Housing

Homeownership rates by state in the US. Some of these figures surprise me: it’s not hard to understand why California and New York might have low homeownership rates due to the high costs of real estate, but Georgia, Texas, and North Dakota being on the low end and West Virginia being on the high end are more surprising to me. [X]

Also on the subject of home ownership, the White House released a report on “Rebuilding and Protecting the American Dream of Homeownership.” It looks at various causes of high housing prices in the US, and concludes with some recommendations for states and local jurisdictions to reduce housing costs:

• Unleashing manufacturing innovation: “...align codes with accepted standards for modular, prefabricated, panelized, and other off-site built housing.”

• Streamlining the stages of homebuilding: “...create a fast-track process for all housing developments that features capped timelines and permit fees, appropriate and justifiable impact fees, third-party inspections, and an expedited appeal process that ensures faster and less arbitrary dispute resolution.”

• Protecting consumer choice and private property rights: “...curtail gratuitous mandates that restrict housing supply, such as restrictions on the number of units that can be built in any given time period, costly green energy building requirements, and discriminatory labor rules.”

Most of these seem like reasonable ideas to me. [White House]

Manufacturing

The Pentagon wants to get US auto manufacturers involved in weapons production, as the wars in Ukraine and Iran run down ordnance stockpiles. This was widely done during WWII, but it’s not obvious how easily today’s car manufacturers could pivot. [WSJ]

Also on the subject of weapons manufacturing, Detroit is angling to be the epicenter of a new US drone manufacturing industry. “Thanks to ramped-up military spending on drones and their proliferation in civilian uses, the market for American-made unmanned aerial systems is expected to grow to more than $50 billion by 2030, from $5 billion this year…Companies are scrambling to build a supply chain from scratch, and states are vying to be at the center of it. In July, Gov. Gretchen Whitmer of Michigan, a Democrat, issued an executive directive calling for a statewide effort to scale up “advanced air mobility” manufacturing, which includes drones and electric planes.” [NYT]

Tulsa, Oklahoma is building the first aluminum smelter built in the US in 50 years, which would double(!) US smelting capacity. [WSJ] Related, this Breakthrough Institute Piece on aluminum and China’s manufacturing prowess has an interesting graphic showing which materials require the most electricity to produce. Titanium requires way more electricity, and electric arc steel requires way less electricity, than I realized. [Breakthrough]

When I looked at welding automation a few years ago, one of the startups I highlighted trying to push automated welding forward was Path Robotics, which at the time was developing a system that could automatically plan out a welding path based on computer vision and a CAD model it had been provided. Now the company just introduced an automated welding system mounted to a robot dog. The utility of this isn’t amazingly obvious to me — I think most welding is probably done in repeatable locations where the dog is unnecessary, in locations that would be tricky for a dog to access, or require some kind of workholding that this doesn’t seem equipped with — but it’s cool nonetheless. [X]

A cool short video clip showing manufacturing of wooden propellers using Blanchard-style pattern-tracing lathes. [X]

Slate Auto, the Jeff Bezos-backed startup that wants to build a no-frills EV truck, raised another $650 million, bringing its total funding to $1.4 billion. [TechCrunch]

Read more

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week we look at whether the Strait of Hormuz is open yet, building code cost benefit analysis, Intel joining Terafab, sponge cities, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

War in Iran

A two-week ceasefire between the US and Iran was announced earlier this week, in exchange for Iran opening the Strait of Hormuz. [BBC] But despite the agreement, so far Iran seems to have kept the Strait closed. [AP News]

“Is Hormuz Open Yet?” is a website for tracking the status of ship crossings in the Strait of Hormuz. [Is Hormuz Open Yet?]

Prior to the cease fire Iran was apparently attempting cyberattacks on US infrastructure. “Iran-affiliated advanced persistent threat (APT) actors are conducting exploitation activity targeting internet-facing operational technology (OT) devices, including programmable logic controllers (PLCs) manufactured by Rockwell Automation/Allen-Bradley. This activity has led to PLC disruptions across several U.S. critical infrastructure sectors through malicious interactions with the project file and manipulation of data on human machine interface (HMI) and supervisory control and data acquisition (SCADA) displays, resulting in operational disruption and financial loss.” [CISA] [LA Times]

Prior to the cease fire Iran threatened to target OpenAI’s Stargate data center in Abu Dhabi. [The Verge] And Microsoft is apparently considering designing more resilient data centers in high-risk areas. [The Register]

This week Iranian drone attacks targeted Saudi Arabia’s Jubail petrochemical complex, [Reuters] UAE’s Habshan gas facility [Al Jazeera], and power and desalination plants in Kuwait. [Al Jazeera]

The US supposedly located the weapons officer of the F-15E shot down in Iran using “Ghost Murmur,” a tool that allegedly uses “long-range quantum magnetometry to find the electromagnetic signal of a human heartbeat.” [NY Post]

Housing

A paper from UCLA’s Lewis Center takes a look at the process of building code development, and notes that provisions added to the building code rarely undergo any sort of cost-benefit analysis. “For example, when a fire marshall in Glendale, Arizona proposed two decades ago that US elevators be required to be larger than international standards to accommodate a 7-ft stretcher lying flat, the cost impact was reported as “none” (Grabar 2025). Today, it is among the reasons that a basic four-stop elevator in New York City costs about $158,000, compared to $36,000 in Switzerland (Smith 2024). These costs are ultimately borne not only as more expensive elevator amenitized buildings, but in the prevalence of newly constructed five- and six-story walk-ups in the US, which are inaccessible to many elderly and disabled tenants and are unheard of in most high-income countries (Smith 2024).” [Escholarship] Building code cost-benefit analysis was one of the points of advocacy from the White House Executive order a few weeks ago. [White House]

Most single-family homes in the US are built to the requirements of the (inaccurately named) International Residential Code. But apartment buildings are built to the somewhat-more-strict International Building Code, even small, house-like apartment buildings like triplexes. The Congress for New Urbanism proposes expanding the IRC to cover small apartment buildings as well. [CNU]

British building standards apparently recommend that windows be sized so that they’re cleanable from the inside by “95% of the elderly female population, without the need for stretching;” if this (non-binding, but often followed in practice) recommendation is followed, the result is extremely tiny windows. [X]

IFP’s infrastructure team has a good piece on how the build-to-rent provisions in the Senate’s ROAD to housing act would dramatically reduce new home construction in the US. (I’m a member of IFP’s infrastructure team, but didn’t help write this piece.) “At President Trump’s request, the Senate acted to prohibit institutional investors from buying up single-family homes. This provision is captured in Section 901 of 21st Century ROAD, “Homes are for people, not corporations.” But while the White House’s Executive Order and proposed legislative text would ban institutional investors from purchasing existing single-family homes, they explicitly protected investor financing of new rental homes. The Senate’s Section 901 goes further: it establishes a disposition requirement for investors to sell their newly built one- and two-family homes within a fixed period, discouraging investment in new rental homes and decreasing housing supply. The last-minute inclusion of Section 901 with this disposition requirement has jeopardized the overall package and fueled calls for a fix in the House. The housing industry, the pro-housing advocacy community, and both Democrats and Republicans in the House and Senate, as well as members of the administration, have voiced opposition to the section as written.” [IFP]

Read more

One such supply chain that’s suddenly getting a lot of attention is helium. Helium is produced as a byproduct of natural gas extraction: it collects in the same underground pockets that natural gas collects in. Qatar is responsible for roughly 1/3rd of the world’s supply of helium, which was formerly transported through the Strait of Hormuz in specialized containers. Thanks to the closure of the strait, helium prices have spiked, suppliers are declaring force majeure, and businesses are scrambling to deal with looming shortages. (For many years the US government maintained a strategic helium reserve, but this was sold off in 2024.)

What I find interesting about helium is that in many cases, it’s very hard to substitute for. Helium has a unique set of properties — in particular, it has a lower melting point and boiling point than any other element — and technologies and processes that rely on those properties can’t easily switch to some other material.

Helium production

Helium is the second lightest element in the periodic table (after hydrogen), and the second most common element in the universe (also after hydrogen). But while helium is very common on a cosmic scale, here on earth it’s not so easy to get. Because helium is so light, it rises to the very top of the atmosphere, where it eventually escapes into space.1 So essentially all helium used by modern civilization comes from underground.

Helium is produced via the radioactive decay of elements like uranium and thorium, and it collects in underground pockets of natural gas. This source of helium was first discovered in the US in 1903, when a natural gas well in Kansas produced a geyser of gas that refused to burn. Scientists at the University of Kansas eventually determined that this was due to the presence of helium. Like petroleum, helium has collected in these pockets over the course of millions of years, and thus (like petroleum) there’s a limited supply of underground helium that can be extracted. As with petroleum, people are often worried that we’re running out of it.

Because helium is a byproduct of natural gas extraction, and because only some natural gas fields have helium in appreciable quantities, a small number of countries are responsible for the world’s supply of helium. The US and Qatar together produce around 2/3rds of the world’s helium supply. Russia, Algeria, Canada, China, and Poland produce most of the remaining balance.

Elemental helium has a few different useful properties. The most important one is that, thanks to the small size and completely filled outer electron shell of helium atoms, helium has a lower boiling point than any other element. Liquid helium boils at just 4.2 kelvin (-452 degrees Fahrenheit). By comparison, liquid hydrogen boils at 20 K, and liquid nitrogen boils at a positively balmy 77 K.

Its low boiling point makes helium very useful for getting something really, really cold. When a liquid boils, it transforms into a gas, and during this process it will pull energy from its surroundings due to evaporative cooling. This is why your body sweats: to cool you down as the liquid evaporates. When a liquid has a very low boiling point, this heat extraction happens at a very low temperature. Helium also stays a liquid at much lower temperatures than other elements. Nitrogen freezes solid at 63 K, and hydrogen freezes at 14K, but at atmospheric pressure helium stays a liquid all the way to absolute zero. If you need to cool something to just a few degrees above absolute zero, liquid helium is essentially the only practical way to do that.

Helium also has a few other useful properties. As we noted, helium is very light: it will naturally rise in the atmosphere, which makes it useful as a lifting gas. Thanks to its filled outer electron shell, it is inert, and won’t react with other materials. Helium also has high thermal conductivity — at room temperature, helium can move heat about six times better than air.

The uses of helium

The world uses around 180 million cubic meters of helium each year. (This sounds like a lot, but it’s just 0.11% of the 159 billion cubic meters of nitrogen the world uses each year, and 0.004% of the over 4 trillion cubic meters of natural gas that the world uses each year.) But while it’s not used in enormous quantities compared to some other gases, helium is nevertheless quite important. Different industries make use of helium’s properties in different ways, and while in some cases there are reasonable substitutes for helium, in most cases helium has no practical replacement.

MRI machines

Some of the biggest consumers of helium are MRI machine operators, which consume around 17% of the helium used in the US. MRI machines work by creating very strong magnetic fields, which change the orientation of hydrogen atoms in tissues in your body. A pulse of radio waves is then sent into your body, which temporarily disrupts this orientation. When the pulse stops, different types of tissue return to their alignment with the magnetic field at different rates, and that rate of change can be measured and converted into a picture of the interior of the body. The strong magnetic fields in MRI machines are created by superconducting magnets: when some materials get cold enough, they drop to zero electrical resistance, which makes it possible to put enormous amounts of electrical current through them and create extremely strong magnetic fields.2 The vast majority of MRI machines used today use superconducting magnets made from niobium-titanium (NbTi), which becomes superconducting at 9.2 degrees above absolute zero. This is well below the boiling point of any other coolant, making liquid helium the only practical option for cooling the magnets. A handful of MRI machines have been built using higher-temperature superconductors that don’t require helium cooling, but the vast majority of the 50,000 existing MRI machines in the world require helium.

The helium consumption of MRI machines has fallen drastically over time. Early MRI machines would lose helium at a rate of around 0.4 liters per hour, requiring large tanks of 1000-2000 liters that needed to be refilled every few months. (It’s notoriously difficult to prevent gaseous helium from leaking out of containers, which is why helium is also often used for leak detection.) But modern MRI machines are “zero boil-off,” which essentially never need to be recharged with helium. As these machines take up more market share, the helium requirements of MRI machines can be expected to fall. But for the foreseeable future, MRI will remain a substantial source of demand.

Semiconductors

Another major consumer of helium is the semiconductor industry, which uses around 25% of the helium worldwide, and around 10% of the helium in the US.3 As with MRI machines, helium is used to cool superconducting magnets, which are used to increase the purity of silicon ingots grown using the Czochralski method. Helium is also used as a coolant in some production processes, as well as a non-reactive gas to flush out some containers, for leak detection, and for a variety of other uses. A 2023 report from the Semiconductor Industry Association noted that helium was used “as a carrier gas, in energy and heat transfer with speed and precision, in reaction mediation, for back side and load lock cooling, in photolithography, in vacuum chambers, and for cleaning.” The same report notes that for many of these uses, helium has no substitute.

Unlike MRI machines, which have used less and less helium over time, helium usage in the semiconductor industry seems to be trending up: some sources claim that helium consumed by the semiconductor industry is expected to rise by a factor of five by 2035. This seems to be in part due to the development of DUV and EUV semiconductor lithography machines, which require helium to function. Unlike many other gases, helium absorbs almost no EUV radiation, which (as I understand it) makes it hard to substitute for helium in EUV machines.

Fiber optics

Helium is also used in the manufacturing of fiber optic cable. Optical cable is made with an inner core of glass, surrounded by an outer “sleeve” of glass with a different index of refraction. This keeps photons within the inner core via the phenomenon of total internal reflection. During the manufacturing process, helium is used as a coolant when the outer “sleeve” is being deposited onto the core — with any other atmosphere, bubbles form between the two layers of glass. Roughly 5-6% of helium worldwide is used for the production of optical fiber, and there’s no known alternative.

Purging gas

Other than semiconductor manufacturing, other industries (particularly the aerospace industry) use helium as a “purge gas” to clean out containers. Cleaning out a tank of liquid hydrogen, often used as a liquid rocket fuel, requires a gas with a boiling point low enough that it won’t freeze when it contacts the hydrogen. Cleaning a tank of liquid oxygen doesn’t require a gas with quite as low a boiling point, but it is best to use an inert gas to reduce the chance of it reacting with the highly reactive oxygen. Aerospace purging makes up around 7% of US helium consumption. Around half of that is used by NASA, which is the single biggest user of helium in the US.

Lifting gas

Because helium is lighter than air, it’s also used as a lifting gas in balloons and lighter-than-air airships as an alternative to the highly flammable hydrogen. Each Goodyear Blimp, for instance, uses around 300,000 cubic feet of helium. Around 18% of the helium consumed in the US is as a lifting gas.

Scientific research and instruments

Helium is also widely used in scientific research. Much of this is for keeping things cold: superconducting magnets, such as those used in the Large Hadron Collider, typically require helium, as do the superconducting elements in SQUIDs, which are highly sensitive magnetic field detectors. Helium is also used in mass spectrometers, which are used for, among other things, detecting microscopic leaks in containers.

This is a major category of use in the US; roughly 22% of its helium consumption goes to “analytical, engineering, lab, science, and specialty gases.”

Welding

In the US, helium is also used for welding: its high thermal conductivity and its inertness make helium an excellent shielding gas, which prevents the pool of molten metal from being contaminated before it cools. In the US, welding makes up roughly 8% of helium use, but elsewhere in the world, it’s more common to use other shielding gases like argon.

Diving

Helium is also used for breathing gas in deep sea commercial diving. At depths beyond 30 meters, breathing nitrogen (which makes up 78% of normal air) causes nitrogen narcosis, and diving beyond this depth is done using gas mixes that replace part of the nitrogen for helium. Roughly 5% of helium consumed in the US goes towards diving.

Helium for diving is difficult to substitute for. Virtually every other breathable gas except for possibly neon causes some degree of narcosis, and neon is heavier than helium, making breathing more difficult.

Conclusion

For some of these applications, it’s possible to substitute helium with other materials. There are other shielding gases, such as argon, that can be used for welding, and other lifting gases, such as hydrogen, that can be used for balloons or airships. In other applications, it’s possible to dramatically reduce the consumption of helium via recycling systems or other methods designed to reduce its use. As we’ve noted, this has occurred with MRI machines, where modern ones use far less helium than their predecessors. And it seems to have happened with aerospace purging. A 2010 report from the National Academies of Sciences notes that if NASA and the Department of Defense were sufficiently motivated, they could dramatically reduce their helium consumption by recycling it. Since then, aerospace use of helium has fallen from 18.2 million cubic meters (26% of total US consumption) to 4 million cubic meters (7% of total US consumption). But the United States Geological Survey notes that most helium in the US is still unrecycled, and there’s lots of opportunity to dramatically reduce helium usage with various recapture and recycling systems. Many of these systems are capable of reducing helium consumption by 90% or more.

But “reducing” doesn’t mean “eliminating,” and it’s interesting to me how in so many cases there doesn’t seem to be any good substitute for helium.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week we look at aluminum disruptions, the EV rust belt, the ongoing transformer shortage, SpaceX’s IPO, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

War in Iran

The world’s largest aluminum smelter in Bahrain was hit by an Iranian drone, bringing production offline. [Bloomberg] Other aluminum smelters in the area have cut production due to inability to ship through the Strait. This, in turn, has forced various EV manufacturers to cut production. “Gulf smelters that supply Toyota, Nissan, BMW, parts makers for Mercedes-Benz, South Korea’s Hyundai Mobis and hundreds of other automotive customers worldwide are defaulting on contracts or closing down. The U.S.-Iran war has effectively shut the Strait of Hormuz to commercial shipping, cutting off one of the largest flows of automotive-grade aluminum.” [Rest of World]

Israel bombed two Iranian steel factories. [NYT] The US bombed an Iranian bridge that was one of the largest in the Middle East. [BBC] And another Amazon data center was damaged by an Iranian drone. [Reuters]

The Philippines declares a National Energy Emergency. [Reuters] And Germany considers ramping up coal power to avert an energy crisis. [Politico]

Helium production in Qatar, which is responsible for roughly 1/3rd of the world’s supply, has been shut down. [NYT] We’ve previously noted that helium is a critical input for semiconductor manufacturing and MRI machines, but it’s apparently also crucial for mass spectrometers used in science labs. [X]

The world is running out of ways to deal with the disruption to oil supply that don’t involve using less oil. “In the first days of this war, the strait’s closure meant the immediate loss of 20 million daily barrels of crude and refined products. The industry went to work, activating a first layer of defense: using up stocks. The second layer came soon after as Saudi Arabia and the United Arab Emirates rerouted some exports using bypass pipelines to Red Sea and Gulf of Oman ports. The third defense came from politicians. The richest nations tapped their strategic reserves, injecting millions of barrels into the market. US President Donald Trump also made constant — and effective — verbal interventions. His jawboning about the chance of an end to the fighting helped tame panic buying.” [Bloomberg]

Italy denied US military aircraft permission to land at a base in Sicily for operations against Iran. [Bloomberg]

Because Iranian ships can traverse the Strait of Hormuz freely, Iran is actually making more money from oil sales than it was prior to the war. “Iran is now earning nearly twice as much from oil sales each day as it did before American and Israeli bombs started falling on February 28th. It may be pummelled on the battlefield, but the regime is winning the energy war.” [The Economist]

Housing

25 housing researchers signed an open letter opposing the provisions in the ROAD to housing act recently passed by the Senate that would limit build-to-rent housing. “If passed, the seven-year disposition requirement would result in a decline of more than 7% of single-family home completions and 18% of rental completions, according to analysis from Laurie Goodman and Jim Parrott at the Urban Institute, a Washington, D.C.-based think tank.” [Multifamily Dive]

Work on what would be the tallest mass timber building in the US (in Milwaukee of all places), has apparently stopped, and the project is facing foreclosure. [Multifamily Dive]

Mortgage rates had been steadily, if slowly, declining over the last year, but since the beginning of the war in Iran they’ve ticked back up. [NYT]

Japanese corporations keep buying US homebuilders. “Japanese builders have announced or closed acquisitions of 23 U.S. single-family home builders since 2020, more than double the number from 2013 to 2019. That doesn’t include the multifamily developers and construction-supply companies they have also bought. By some estimates, Japanese builders are now set to own about 6% of the U.S. home-construction market.” [WSJ]

Read more

But if you’re trying to understand the nature of technological progress more generally, the range of good options narrows significantly. There’s probably not more than ten or twenty folks who have studied the nature of technological progress itself and whose work I think is worth reading.

One such researcher is Brian Arthur, an economist at the Santa Fe Institute.1 Arthur is the author of an extremely good book about the nature of technology (called, appropriately, “The Nature of Technology,”) which I often return to. He’s also the co-author, along with Wolfgang Polak, of an interesting 2006 paper, “The Evolution of Technology within a Simple Computer Model,” that I think is worth highlighting. In this paper Arthur evolves various boolean logic circuits (circuits that take ones and zeroes as inputs and give ones and zeroes as outputs) by starting with simple building blocks and gradually building up more and more complex functions (such as a circuit that can add two eight-bit numbers together).

I wanted to highlight this paper because I think it sheds some light on the nature of technological progress, but also because the paper does a somewhat poor job of articulating the most important takeaways. Some of what the paper focuses on — like the mechanics of how one technology gets replaced by a superior technology — I don’t actually think are particularly illuminating. By contrast, what I think is the most important aspect of the paper — how creating some new technology requires successfully navigating enormous search spaces — is only touched on vaguely and obliquely. But with a little additional work, we can flesh out and strengthen some of these ideas. And when we look a little closer, we find what the paper is really showing us is that finding some new technology is a question of efficiently acquiring information.

Outline of the paper

The basic design of the experiment is simple: run a simulation that randomly generates various boolean logic circuits and analyze the sort of circuits that the simulation generates. Boolean logic circuits are collections of various functions (such as AND, OR, NOT, EQUAL) that perform some particular operation on binary numbers. The logic circuit below, for instance, determines whether two 4-bit numbers are equal using four exclusive nor (XNOR) gates, which output a 1 if both inputs are identical, and a 4-way AND gate, which outputs a 1 if all inputs are 1. Boolean logic circuits are important because they’re how computers are built: a modern computer does its computation by way of billions and billions of transistors arranged in various logic circuits.

The simulation works by starting with three basic circuit elements that can be included in the randomly generated circuits: the Not And (NAND) gate (which outputs 0 if both inputs are 1, and 1 otherwise), and two CONST elements which always output either 1 or 0. The NAND gate is particularly important because NAND is functionally complete; any boolean logic circuit can be built through the proper arrangement of NAND gates.

Using these starting elements, the simulation tries to build up towards higher-level logical functions. Some of these goals, such as creating the OR, AND, and exclusive-or (XOR) functions, are simple, and can be completed with just a few starting elements. Others are extremely complex, and require dozens of starting elements to implement: an 8-bit adder, for instance, requires 68 properly arranged NAND gates.

To achieve these goals, during each iteration the simulation randomly combines several circuit elements — which at the beginning are just NAND, one, and zero. It randomly selects between two and 12 components, wires them together randomly, and looks to see if the outputs of the resulting circuit achieve any of its goals. If it has — if, by chance, the random combination of elements has created an AND function, or an XOR function, or any of its other goals — that goal is marked as fulfilled, and circuit that fulfills it gets “encapsulated,” added to the pool of possible circuit elements. Once the simulation finds an arrangement of NAND components that produces AND and OR, for instance, those AND and OR arrangements get added to the pool of circuit elements with NAND and the two CONSTS. Future iterations thus might accidentally stumble across XOR by combining AND, OR, and NAND.

Because finding an exact match for a given goal might be hard, especially as goals get more complex, the simulation will also add a given circuit to the pool of usable components if it partially fulfills a goal, as long as it does a better job of meeting that goal than any existing circuit. Circuits that partially meet some goal (such as a 4-bit adder that gets just the last digit wrong) are similarly used as components that can be recombined with other elements. So the simulation might try wiring up our partly-correct 4-bit adder with other elements (NAND, OR, etc.) to see what it gets; maybe it finds another mini-circuit that can correct that last digit.

Over time, the pool of circuit elements that the simulation randomly draws from grows larger and larger, filled both with circuits that completely satisfy various goals and some partly-working circuits. A circuit can also get added to the pool if it’s less expensive — uses fewer components — than existing circuits for that goal. So if the simulation has a 2-bit adder made from 10 components, but stumbles across a 2-bit adder made from 8 components, the 8-component adder will replace the 10 component one.

When the simulation is run, it begins randomly combining components, which at the beginning are just NAND, one, and zero. At first only simple goals are fulfilled: OR, AND, NOT, etc. The circuits that meet these goals then become building blocks for more complex goals. Once a 4-way AND gate is found (which outputs 1 only if all its inputs are 1), that can be used to build a 5-way AND gate, which in turn can be used to build a 6-way AND gate. Over several hundred thousand iterations, surprisingly complex circuits can be generated: circuits which compare whether two 4-bit numbers are equal, circuits which add two 8-bit numbers together, and so on.

However, if the simpler goals aren’t met first, the simulation won’t find solutions to the more complex goals. If you remove a full-adder from the list of goals, the simulation will never find the more complex 2-bit adder. Per Arthur, this demonstrates the importance of using simpler technologies as “stepping stones” to more complex ones, and how technologies consist of hierarchical arrangements of sub-technologies (which is a major focus of his book).

We find that our artificial system can create complicated technologies (circuits), but only by first creating simpler ones as building blocks. Our results mirror Lenski et al.’s: that complex features can be created in biological evolution only if simpler functions are first favored and act as stepping stones.

Analyzing this paper

I don’t have access to the original simulation that Arthur ran, but thanks to modern AI tools it was relatively easy for me to recreate it and replicate many of these results. Running it for a million iterations, I was able to build up to several complex goals: 6-bit equal, a full-adder (which adds 3 1-bit inputs together), 7-bitwise-XOR, and even a 15-way AND circuit.

But I also found that not all of the simulation design elements from the original paper are load-bearing, at least in my recreated version. In particular, much of the simulation is devoted to the complex “partial fulfillment” mechanic, which adds circuits that only partially meet goals, and gradually replaces them as circuits that better meet those goals are found. The intent of this mechanic, I think, is to make it possible to gradually converge on a goal by building off of partly-working technologies, which is how real-world technologies come about. However, when I turn this mechanic off, forcing the simulation to discard any circuit that doesn’t 100% fulfill some goal, I get no real difference in how many goals get found: the partial fulfillment mechanic basically adds nothing (though this could be due to differences in how the simulations were implemented).

To me the most interesting aspect of this paper isn’t showing how new, better technologies supersede earlier ones, but how the search for a new technology requires navigating enormous search spaces. Finding complex functions like an 8-bit adder or a 6-bit equal requires successfully finding working functions amidst a vast ocean of non-working ones. Let me show you what I mean.

We can define a particular boolean logic function with a truth table – an enumeration of every possible combination of inputs and outputs. The truth table for an AND function, for instance, which outputs a 1 if both inputs are 1 and 0 otherwise, looks like this:

Every logic function will have a unique truth table, and for a given number of inputs and outputs there are only so many possible logic functions, so many possible truth tables. For instance, there are only four possible 1 input, 1 output functions.

However, the space of possible logic functions gets very very large, very very quickly. For a function with n inputs and m outputs, the number of possible truth tables is (2^m)^(2^n). So if you have 2 inputs and 1 output, there are 2^4 = 16 possible functions (AND, NAND, OR, NOR, XOR, XNOR, and 10 others). If you have 3 inputs and 2 outputs, that rises to 4^8 = 65,536 possible logic functions. If you have 16 inputs and 9 outputs, like an 8-bit adder does, you have a mind-boggling 10^177554 possible logic functions. By comparison, the number of atoms in the universe is estimated to be on the order of 10^80, and the number of milliseconds since the big bang is on the order of 4x10^20. Fulfilling some goal from circuit space means finding one particular function in a gargantuan sea of possibilities.

The question is, how is the simulation able to navigate this enormous search space? Arthur touches on the answer — proceeding to complex goals by way of simpler goals — but he doesn’t really look deeply at the combinatorics in the paper, or how this navigation happens specifically.2

The emergence of circuits such as 8-bit adders seems not difficult. But consider the combinatorics. If a component has n inputs and m outputs there are (2^m)^(2^n) possible phenotypes, each of which could be realized in a practical way by a large number of different circuits. For example, an 8-bit adder is one of over 10^177,554 phenotypes with 16 inputs and 9 outputs. The likelihood of such a circuit being discovered by random combinations in 250,000 steps is negligible. Our experiment— or algorithm—arrives at complicated circuits by first satisfying simpler needs and using the results as building blocks to bootstrap its way to satisfy more complex ones.

Navigating large search spaces

In his 1962 paper “The Architecture of Complexity,” Nobel Prize-winning economist Herbert Simon describes two hypothetical watchmakers, Hora and Tempus. Each makes watches with 1000 parts in them, and assembles them one part at a time. Tempus’ watches are built in such a way that if the watchmaker gets interrupted — if he has to put down the watch to, say, answer the phone — the assembly falls apart, and he has to start all over. Hora’s watches, on the other hand, are made from stable subassemblies. Ten parts get put together to form a level 1 assembly, ten level 1 assemblies get put together to form a level 2 assembly, and 10 level 2 assemblies get put together to form the final watch. If Hora is interrupted in the middle of a subassembly, it falls to pieces just like Tempus’ watches, but once a subassembly is complete it’s stable; he can put it down and move on to the next assembly.

It’s easy to see that Tempus will make far fewer watches than Hora. If both have a 1% chance of getting interrupted each time they put in a part, Tempus only has a 0.99 ^ 1000 = 0.0043% chance of assembling a completed watch; the vast majority of the time, the entire watch falls to pieces before he can finish. But when Hora gets interrupted, he doesn’t have to start completely over, just from the last stable subassembly. The result is that Hora makes completed watches about 4,000 times faster than Tempus.

Simon uses this model to illustrate how complex biological systems might have evolved; if a biological system is some assemblage of chemicals, it’s much more likely for those chemicals to come together by chance if some small subset of them can form a stable subassembly. But we can also use the Tempus/Hora model to describe the technological evolution being simulated in Arthur’s paper.

Consider a technology as some particular arrangement of 1,000 different parts, such as the NAND gates that are the basic building blocks of Arthur’s logic circuits. If you can find the proper arrangement of parts, you can build a working technology. Assume we try to build a technology by adding one part at a time, like Tempus and Hora build their watches, until all 1000 parts have been added. In this version, instead of having some small probability of being interrupted and needing to start over, we have a small probability (say 1%) of correctly guessing the next component. This mirrors Arthur’s simulation, where we had a small probability of randomly connecting a component correctly to fulfill some goal. Only by properly guessing the arrangement of each part, in order, can we create a working technology.

In Simon’s original model, assembling a watch was like flipping 1000 biased coins in a row. Each coin had a 99% chance of coming up heads, and only when 1000 heads were flipped was a watch successfully assembled. Our modified model is like flipping 1000 biased coins which have only a 1% chance of coming up heads. Creating a technology via the “Tempus” method is like flipping 1000 coins in a row and hoping for heads each time. The probability of producing a working technology is 0.01^1000, essentially zero. But if we create a technology via the “Hora” method of building it out of stable subassemblies, the combinatorics become much less punishing. Now instead of needing to flip 1000 heads in a row, we only need to flip 10 in a row. 10 successful coinflips — 10 parts successfully added — gives us a stable subassembly, letting us essentially “save our place.” Flipping a tails doesn’t send us all the way back to zero, just to the last stable subassembly. The odds are still low — for each subassembly, you only have a 0.01^10 chance of getting it right — but it’s enormously higher than the Tempus design. You’re much more likely to stumble across a working technology if that technology is composed of simpler stable components, and you can determine whether the individual components are correct.

Arthur’s circuit simulation is able to find complex technologies because it works like Hora, not Tempus: complex circuits are built up from simpler technologies, the way Hora’s watches are built from stable subassemblies. Going from nothing to an 8-bit adder is like Tempus trying to build an entire and very complex watch by getting every step perfect. Much easier to be like Hora, and be the one that only needs to get the next few steps to a stable subassembly correct: adding a few components to a 6-bit adder to get a 7-bit adder, then adding a few to that one to get an 8-bit adder, and so on.

We can illustrate this more clearly with a modified version of Arthur’s circuit search. In this version, rather than trying to fulfill a huge collection of goals, we’re just trying to find the design for a specific 8-bit adder made from 68 NAND gates. Rather than build this up from simpler sub-components (7-bit adders, 6-bit adders, full adders), in this simulation we simply go NAND gate by NAND gate. Each iteration we add a NAND gate, and randomly wire it to our existing set of NAND gates. If we get the wiring correct, we keep it, and go on to try adding the next gate. If it’s incorrect, we discard it and try again.

We can think of this as a sort of modular construction, akin to building a complex circuit up from simpler circuits; at each level, we’re just combining two components, our existing subassembly and one additional NAND gate. This loses verisimilitude, since each subassembly no longer implements some particular functionality (we essentially just dictate that the simulation knows when it stumbles upon the correct gate wiring). But we don’t lose that much: it is, notably, possible to build an 8-bit adder with a hierarchy that requires just two components at almost every level (a few steps require three components). And this simpler simulation has the benefit of making it very easy to calculate the combinatorics at each step.

68 NAND gates can create around 2^852 possible wiring arrangements with 16 inputs and 9 outputs. This is much less than the 10^177,554 possibile 16 input and 9 output functions, but it’s still an outrageously enormous number. If we tried to find the right wiring arrangement by random guessing all 68 gates at once, we’d never succeed: even if every atom in the universe was a computer, each one trying a trillion guesses a second, we’d still be guessing for about 10,000,000,000,000…(140 more zeroes)..000 years.

But by going gate-by-gate, the correct arrangement can be found in 453,000 iterations, on average. Each time we add a gate, there’s only a few thousand possible ways that it can be connected, so after a few thousand iterations we guess it correctly, lock the answer in, and move on to the next gate. By determining whether each step is correct, instead of trying to guess the complete answer all at once, the search becomes feasible.

This is why Arthur’s original simulation couldn’t fulfill complex needs without fulfilling simpler needs first: if you try to take too many steps at once, the combinatorics become too punishing, and it becomes almost impossible to find the correct answer by random guessing. In our 68 NAND gate search, finding an 8-bit adder is relatively easy if we go one gate at a time, but if we change that to two gates at a time (randomly adding one gate, then another gate, then checking to see if we’re correct), the expected number of iterations rises from 453,000 to 1.75 billion: if the probability of guessing one gate correctly is 1/1,000, the probability of guessing two gates is 1/1,000,000. If we try to guess three gates at a time (1 in a billion odds of guessing correctly), the number of expected iterations to guess all 68 gates correctly rises to ~9.3 trillion.

The explosive combinatorics gives us a better understanding of some of the results that come out of Arthur’s simulation. For instance, Arthur notes that in each iteration the simulation combines up to twelve components, then checks to see if a working circuit has been found. But Arthur notes that you can vary the maximum value and it doesn’t impact the results of the simulation much, stating of the various simulation settings that “[e]xtensive experiments with different settings have shown that our results are not particularly sensitive to the choice of these parameters.” Indeed, if we re-run the simulation and only allow it to try a maximum of 4 components at once, it works basically just as well as with 12 components. The more random components you combine together, the more the combinatorial possibilities explode, and the lower the chance of finding something useful. The probabilities of finding a useful circuit amongst the various possibilities becomes so immensely low with larger numbers of components that you don’t lose much by not bothering with them at all. Similarly, this also explains another result in the paper, that it’s easier to find complex goals if you specify only a narrower subset of simpler goals related to them. Arthur notes that a complex 8-bit adder is found much more quickly if you only give the simulation a few goals related to building adders. With fewer goals specified, the pool of possible technology components will remain smaller, the number of possible random combinations becomes fewer, and the easier it becomes to find the complex goals.

In essence, using simpler components as stepping stones to more complex ones is a kind of hill-climbing. The simulation looks in various directions (possible combinations of building blocks), until it finds one that’s higher up the hill (finds a circuit that meets some simple goal), restarts the search at the new, higher point on the hill, until it reaches a peak (satisfies a complex goal). The simulation is able to satisfy complex goals because it specified a series of simpler ones that provide a path up the hill to the complex goals. Arthur notes that “[t]he algorithm works best in spaces where needs are ordered (achievable by repetitive pattern), so that complexity can bootstrap itself by exploiting regularities in constructing complicated objects from simpler ones.”

Trying to go to complex circuits directly, then, is akin to just testing random locations in the landscape and seeing if they’re a high point: this is obviously much worse than following the slope of the landscape to find the high points.

Technological search and information

We can sharpen these ideas even further by bringing in some concepts from information theory. Information theory was invented by Claude Shannon at Bell Labs in the late 1940s, and it provides a framework for quantifying your uncertainty, and how much a given event reduces that uncertainty.

I find the easiest way to understand information theory is with binary numbers. The normal math we use day to day uses base 10 numbers. When we count upward from zero, we go from 0 to 9, then reset the first digit to 0 and increment the next digit: 10. With binary, or base 2, we increment the next digit after we get to 1. So 1 in base 10 is 1 in binary, but 2 is 10, 3 is 11, 4 is 100, and so on.

In binary, each binary digit, or bit, doubles the potential size of the number we can represent. So with two digits, we can define 4 possible values (0, 1, 2, and 3 in base 10). With 3 digits, that doubles to 8 possible values (getting us from 0 through 7), with 4 digits that doubles again to 16 possible values, and so on. A 16 bit binary number can represent 2^16 = 65,536 possible values, which is why in computer programming the largest value that a 16 bit integer can represent is 65,535.

Say you have a string of bits, but don’t know whether they’re ones or zeroes. Because each bit doubles the number of possible values that can be represented, each unknown bit you fill in reduces the number of possible values by half. If you have 3 binary digits, there are 8 possible numbers that could be represented. Each time you learn what one of the bits is, you reduce the number of possible values by half.

With information theory, we generalize this concept somewhat. In information theory one bit of information reduces the space of possibilities by 50%; in other words, each bit reduces our uncertainty by half. Say you’re like me, and you often lose your phone in your jacket pockets. If you’re wearing a coat with 2 pockets and you know the phone is in one of them, specifying the location of your phone, narrowing it down from 2 possibilities to 1, takes one bit of information. If you’re wearing a coat with 4 pockets, you now need 2 bits of information: 1 bit to tell you whether it’s on the right or the left, and another bit to tell you whether it’s an upper or lower pocket. The first bit cuts the possibilities in half, leaving you with two possibilities, and the second bit cuts it in half again. If your jacket has 8 pockets, now you need 3 bits to specify its location, and so on. The more places that something could be, the more information it takes to specify its location.

Information theory is particularly useful for quantifying how much information we get from some particular outcome. Say someone flips a fair coin; how much information do I get when they reveal whether it was heads or tails? Well, before they reveal it, I knew it could be one of two options, heads or tails. Revealing it narrows the number of possibilities from two down to one. We’ve cut the number of possibilities in half, and thus gained 1 bit of information. More generally, the information provided by some outcome is equal to -log2(the probability of that outcome). So revealing how a fair coin was flipped gives us -log2(0.5) = 1 bit. If we’re dealt a single card from a deck face down, when we reveal that card we’ve reduced the number of possible cards from 52 down to 1, and gained -log2(1/52) = 5.7 bits of information.

For a repetitive process, we also want to know a related quantity: entropy. Entropy is determined by calculating the information received from each possible outcome, multiplying it by the probability of that outcome, then summing all those values together. It’s the expected quantity of information you’ll get by taking some particular action.

Say I’ve lost my phone in my jacket with eight pockets, and am looking for it by randomly trying pockets until I find it. A random guess has a 1/8 chance of successfully finding the phone, and a 7/8 chance of coming up empty. Guessing correctly will yield me -log2(1/8) = 3 bits of information, as expected: once I guess correctly, I know the phone’s location. But an incorrect guess will yield me only 0.19 bits of information: I already knew most of the pockets don’t have the phone, so failing to find the phone in one pocket doesn’t tell me much that I didn’t already know. The entropy of a guess is (1/8) * log2(1/8) + (7/8) * log2(7/8) = 0.54 bits. When I first check a pocket, I can expect to get a little more than half a bit of information. (If I rule out pockets that I’ve already checked, the expected amount of information I get will rise each time, though if you’re like me you might have to check the same pocket a few times before you find the phone.)

Because each bit of information we get cuts the number of possibilities remaining by 50%, it doesn’t take that much information to narrow down an enormously large search space. The 2^852 possible circuits that can be created by wiring up our 68 NAND gates requires only 852 bits — 852 times cutting the number of possibilities in half — to specify. (That’s approximately the same number of bits that it takes to specify each letter of this sentence.)

A key aspect of entropy is that we maximize how much information we get when each outcome is equally plausible. So the entropy of a fair coin, with a 50% chance of coming up heads, is 1 bit. But if the coin has a 90% chance of coming up heads, the entropy is now just 0.46 bits. If the coin has a 99% chance of coming up heads, the entropy falls to 0.08 bits. When one outcome is very likely, you learn much less on each attempt, because you mostly get the outcome you already knew was likely. This is why when playing the game “20 questions,” the most efficient strategy is to try and ask questions where the answer divides the number of possibilities in half. “Is it bigger than a breadbox?” is a good starting question because there are probably roughly similar numbers of items that are bigger and smaller than a breadbox. “Is it a 1997 Nissan Sentra?” is a bad starting question because most possibilities are not a 1997 Nissan Sentra, so we learn very little when the answer is “no.”

We can think of our 68 NAND gate search as flipping a series of very biased coins, each one with a ~ 1/(several thousand) probability of coming up heads (where “heads” is “guessing the right wiring combination for that particular NAND gate”). The entropy of this process — the expected amount of information that we get — is very low, around 0.003 bits per attempt. Each attempt we learn very little about the correct wiring diagram (“it wasn’t this arrangement, it wasn’t this arrangement either, or this one”) so we need a lot of attempts — around 453,000, on average — to accumulate the 852 bits needed to specify the correct wiring for our 8-bit adder.3

Trying to guess two gates at a time is like biasing the coin even further: now each one has a ~1/(several million) probability of coming up heads. We thus get vastly less information per attempt — less than 0.000001 bits per attempt on average — so it takes us many, many more attempts to accumulate the information needed.

A useful way of thinking about our 68 NAND gate search is that it’s like a huge, branching tree. At every step — every time we add a gate — there are thousands of branches, each one representing one possible way to wire up the gate. Each branch then splits into thousands more (representing all the possible ways of wiring up the next gate), which split into thousands more, which split into thousands more, until at the end we have 2^852 possible “leaves,” each one representing a unique way of wiring up all 68 gates. Trying to get all 68 gates right at once, and then checking to see whether or not you did, is like examining one single leaf, one path from the base of the tree all the way to the tip of a branch. Not only are you overwhelmingly likely to guess wrong, but you haven’t narrowed down your possibilities at all: all you know is that one single leaf wasn’t the right answer, leaving you with the rest of the 2^852 possible leaves to sift through.

Checking to see whether each gate we add is correct before we proceed to the next one, by contrast, massively narrows down the number of possibilities. Whenever we determine a gate isn’t in the right spot, it eliminates every possibility that branches off from that point. If there are 1000 possible ways to wire each gate, each time we guess correctly we’ve narrowed down the possibilities downstream of that choicepoint by 99.9%. Huge swaths of possibilities get eliminated at each correct guess, letting us converge on the correct answer much more quickly.

The same basic logic applies to Arthur’s simulation. (In fact, in another publication, Arthur uses a very similar metaphor, describing technological search as trying to find a working path up a mountain, which is full of various obstacles.) Building up complex functions without the aid of intermediate, simpler ones is like trying to find a single leaf on a tree the size of the universe. Building up to complex circuits gradually, using simpler components as building blocks, lets you screen off huge branches of the tree at once. Once you have a working 2-bit adder, every branch that has a non-working 2-bit adder in it gets screened off. Your iterations yield massively more information, and the search problem becomes tractable.

Conclusion

The logic of Arthur’s simulation, and our simpler simulation, also applies to creating new technologies more generally. Logic circuits are a useful model to explore, because they’re real technology that is very amenable to simulation (they have a well-defined, simple behavior), but technology in general can be thought of as a combination of simpler components or elements arranged in various ways to create more complex ones. As Arthur notes:

…in 1912 the amplifier circuit was constructed from the already existing triode vacuum tube in combination with other existing circuit components. The amplifier in turn made possible the oscillator (which could generate pure sine waves), and these with other components made possible the heterodyne mixer (which could shift signals’ frequencies). These two components in combination with other standard ones went on to make possible continuouswave radio transmitters and receivers. And these in conjunction with still other elements made possible radio broadcasting. In its collective sense, technology forms a network of elements in which novel elements are continually constructed from existing ones. Over time, this set bootstraps itself by combining simple elements to construct more complicated ones and by using few building-block elements to create many.

One takeaway from this paper, as Arthur notes and we explored more deeply, is that a hierarchical arrangement of components, where a complex technology is made of simpler components, which are in turn made from even simpler components, makes it much easier to create some new technology. But a more general takeaway is that successfully creating some new technology means getting new information as quickly as possible. Working from (or towards) a hierarchical, modular design for some technology, where each element has some specific job it must do, makes it easier to find new technologies in part because you learn vastly more from each attempt at building one of those subparts. Knowing whether some entire complex function works or not tells you much less than knowing which individual component is working right, and what specific functionality needs to be corrected to fix the problem.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure and industrial technology. This week we look at plastic price jumps, crypto-backed mortgages, a proposed AI data center pause, US battery manufacturing, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

War in Iran

The disruption to oil and LNG supplies caused by the closure of the Strait of Hormuz is causing some countries to burn more coal. “India is burning more coal to meet higher summer demand. South Korea has lifted caps on electricity from coal. Indonesia is prioritizing using its domestic supply. Thailand, the Philippines and Vietnam are boosting coal-fired power.” [NPR]

And because petroleum is used to make plastic, the closure of the strait is also driving up plastic prices. Dow Chemical plans a 30 cents per pound increase in April, following a 10 centers per pound increase in March. [WSJ] Plastic prices are up nearly 40% since February. [Reuters]

Petroleum is also used in the manufacture of pharmaceuticals, so the strait closure might affect the supply of generic medications. [CNBC] And last week I noted that production of helium (which is extracted as part of the natural gas drilling process) had also been disrupted, but I hadn’t clocked that liquified helium is used as a coolant for MRI machines. [X]

Another AWS data center in the mideast has apparently been damaged by an Iranian drone attack. [Al Jazeera]

To try and address rising gas prices, the EPA is temporarily waiving regulations on the sale of ethanol blended gas (which is restricted in certain locations at certain times of the year to reduce air pollution). [CNBC]

Iran has established a “safe shipping corridor” through the strait, allowing ships to pass for a $2m fee. [Lloyd’s List]

The closure of the strait seems to be good for Chinese battery manufacturers though: since the start of the war their stocks have spiked. “CATL’s China-traded shares have risen 19 per cent; Sungrow is up 19.4 per cent; and BYD, also the world’s top EV maker, has gained 21.9 per cent since the US-Israeli strikes were launched at the end of February.” [FT]

Housing

The New York Times on the ROAD to Housing Act, and the provision that would prevent the construction of build-to-rent single family homes. “Now, with the legislation back in the House awaiting a vote, critics are urging lawmakers to drop that build-to-rent restriction, arguing it would counter the intent of the bill, making it harder, not easier, to build homes when the country desperately needs them.” [NYT] Senator Elizabeth Warren is apparently unhappy about the resistance to these provisions of the bill, and is sending vaguely threatening letters to investors in multifamily apartments and manufactured homes. [Axios]

Another NYT article on the surprising success of some US malls. Apparently malls with higher-end, luxury tenants are doing fairly well. “There are roughly 900 malls in the United States, but only a small sliver are successful. The top 100 account for 50 percent of the entire sector’s value, according to Tibone, whereas the bottom 350 make up 10 percent. Revenue at class A malls is growing by 5 percent each year, and financing is easy to come by.” This is not totally surprising to me — malls in the Atlanta metro area seem crowded whenever I drive by or go in one, so I knew some must be succeeding — but it’s interesting to see that it seems to be specifically the high-end malls. [NYT]

Fannie Mae will now allow mortgages backed with cryptocurrency. “The mortgage company Better Home & Finance and the U.S. crypto exchange Coinbase Global unveiled a new mortgage product Thursday that allows home buyers to pledge their crypto holdings when getting a Fannie-backed mortgage, instead of selling the crypto to make a cash down payment.” What could go wrong. [WSJ]

Manufacturing

Elon Musk announces “Terafab” semiconductor fab to make chips for Tesla cars, Optimus, and space-based data centers. “We either build the Terafab or we don’t have the chips,” he said while on stage at an event in Austin. “We need the chips, so we’re going to build the Terafab.” [WSJ]

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