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]

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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]

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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]

Read more

As we’ve noted more than a few times before, for most of the 20th century AT&T’s Bell Labs was the premier industrial research lab in the US. As part of its ongoing efforts to provide universal telephone service, Bell Labs generated numerous world-changing inventions, and accumulated more Nobel Prizes than any other industrial research lab.1 But the most important of its technical contributions proved to be useful far beyond the confines of the Bell System. Statistical process control, for instance, was invented by AT&T engineer Walter Shewhart to improve the manufacturing of AT&T’s electrical equipment at supplier company Western Electric. Since then, the methods have been successfully applied to all manner of manufacturing, from jet engines to semiconductors to container ships.

Interestingly, some of AT&T’s most important technological contributions — namely, the vacuum tube, the negative feedback amplifier, the transistor, and the laser — were (in whole or in part) the product of efforts to make new, better amplifiers for boosting electromagnetic signals. Amplifiers played a crucial role in the Bell System, making it possible to (among other things) connect telephones over long distances, but the value of these four amplifiers extended far beyond telephony. The vacuum tube became a crucial building block for electronics in the first half of the 20th century, used in everything from radio to television to the earliest computers. The negative feedback amplifier helped spawn the discipline of control theory, which is used today in the design of virtually every automated machine. The transistor is the foundation of modern digital computing and everything built on top of it. And the laser is used in everything from fiber-optic communications to industrial cutting machines to barcode scanners to printers.

It’s worth looking at why AT&T was so motivated to build better and better amplifiers, and why those efforts produced so many transformative inventions.

The vacuum tube

In 1876 Alexander Graham Bell placed the world’s first telephone call, summoning his assistant Thomas Watson from another room. By 1881, Bell’s company, the Bell Telephone Company (it wouldn’t become American Telephone and Telegraph, or AT&T, until 1899) had 100,000 customers. By the turn of the century AT&T was operating 1,300 telephone exchanges in the US, connecting over 800,000 customers with 2 million miles of wire.

The goal of the Bell System was “universal service” – to connect every telephone user with every other telephone user in the system. But by the early 20th century this quest was bumping up against technological limitations.

Telephones converted the sound of someone speaking to electrical signals, which were transmitted along wires until reaching a telephone on the other end, where they were converted back into sound. More specifically, in early telephones the sound from someone speaking would compress and decompress a chamber full of carbon granules, which would alter their electrical resistance, changing how much current flowed through them. At the other end, the electrical current would flow through an electromagnet, which pulled on a thin iron diaphragm; fluctuations in the electrical current would change the motion of the diaphragm, reproducing the speech.

But the farther electrical signals travelled, the more they would be attenuated. Resistance from the wire carrying them would convert some of the electrical energy into heat, and electrical current could “leak” between adjacent telephone wires. As the electrical signals got weaker and weaker, the sound would be less and less intelligible when reproduced, until it couldn’t be heard at all. If AT&T wanted to provide universal service, it would need a way to maintain the strength of the electrical signal as it traveled over long distances.

AT&T was able to partly resolve this problem using the loading coil, an invention of electrical engineer Michael Pupin. (Lines which had loading coils added to them were sometimes described as being “Pupinized.”) The loading coil added inductance (a tendency to resist changes in current) to telephone lines, which reduced signal attenuation. As a result, the loading coil roughly doubled the effective distance limit of telephone calls, from around 1000-1200 miles to closer to 2000 miles. But the loading coil merely reduced signal attenuation; the signal was still decaying as it traveled along the lines, just more slowly. Without some way of actually amplifying the telephone signals, the maximum distance for a telephone line was enough to connect New York to Denver, but not enough to reach the West Coast from New York and connect the entire country.

AT&T experimented with various mechanical amplifiers, which converted the electrical signals into mechanical movements and then back to electrical signals, but these were found to greatly distort the signal, and were not widely used. What was needed was an electronic amplifier, which could amplify the electrical signals directly, without the lossy and distorting effects of mechanical translation. In 1911, AT&T formed a special research branch to tackle the problem of long-distance transmission, and hired the young physicist Harold Arnold (who would later become the first director of research at Bell Labs) to research possible amplifiers based on the “new physics” of electrons.

At first, Arnold had little success. He looked at a variety of possible amplifying technologies, and experimented extensively with mercury discharge tubes (which initially seemed promising), but nothing appeared to fit AT&T’s requirements. But in 1912, Arnold learned of a new, promising amplifier known as the audion, which had been brought to AT&T by American inventor Lee de Forest. De Forest’s audion was, in turn, based on an invention of the British physicist Ambrose Fleming, known as the “Fleming valve.” Fleming was inspired by extensive experimentation with what was known as the “Edison Effect:” the observation that in an incandescent bulb, electric current would flow from the heated filament to a nearby metal plate. Fleming used this effect to create a diode, a device which lets electric current flow in one direction but not another. De Forest modified Fleming’s valve by adding a third element, a metallic grid, between the filament and the plate. By varying the voltage at the metallic grid, De Forest eventually discovered he could control the flow of electrical current from the filament to the plate. This allowed the device to act as an amplifier: a small change in the voltage could create a much larger change in the current flowing from the filament to the plate.

De Forest’s audion had uneven performance — notably, it couldn’t handle the level of energy needed for a telephone line. Moreover, it was clear that De Forest did not quite understand how the device worked. But Arnold, well-versed in the physics of electrons, recognized its potential, and realized that, with modifications, its various limitations could be overcome. Via “The Continuous Wave,” a history of early radio:

Arnold knew exactly what to do about the audion’s limitations. “I suggested that we make the thing larger, increase the size of the plate with the corresponding increases in the size of the grid but particularly at that time I suggested that we were not getting enough electrons from the filament.” What he wanted to do, in fact, was convert the de Forest audion into a different kind of device. He wanted a much higher vacuum in the tube, with residual gas eliminated to the greatest possible extent; and he knew the newly invented Gaede molecular vacuum pump made that possible. He wanted more electron emission from the filament without an increase in filament voltage; and he knew Wehnelt’s new oxide-coated filaments would do that.

After paying $50,000 (roughly $1.6 million in 2026 dollars) for the rights to the audion, Arnold and others at AT&T spent the next year turning it into a practical electronic amplifier: the triode vacuum tube. By June 1914, vacuum tube amplifiers were being installed on a transcontinental telephone line connecting New York and San Francisco, and in January of 1915 the transcontinental line was inaugurated at the Panama-Pacific International Exposition with a call between Alexander Graham Bell in New York and Thomas Watson in San Francisco. By the late 1920s, AT&T was using over 100,000 vacuum tubes in its telephone system, and triodes and their descendents (four-element tetrodes, five-element pentodes) would go on to be used in all manner of electronic devices, from radios to TVs to the first digital computers.

The vacuum tube, with its ability to amplify electronic signals, represented a sea change in how AT&T engineers thought about telephone service. Prior to the electronic amplifier, a telephone call was essentially a single diminishing stream of electromagnetic energy. The range of that energy could be extended farther and farther from the speaker at the steep cost of its fidelity. The amplifier made it possible to consider a telephone call as a stream of information, as a signal that was distinct from the medium that carried it. It could be ably renewed, translated, modified in new and exciting ways. As historian David Mindell notes:

…a working amplifier could renew the signal at any point, and hence maintain it through complicated manipulations, making possible long strings of filters, modulators, and transmission lines. Electricity in the wires became merely a carrier of messages, not a source of power, and hence opened the door to new ways of thinking about communications…The message was no longer the medium, now it was a signal that could be understood and manipulated on its own terms, independent of its physical embodiment.

The negative feedback amplifier

Thanks to vacuum tube amplifiers, AT&T could finally fulfill its dream of universal telephone service, connecting telephones to each other anywhere in the continental US. But vacuum tubes were far from perfect amplifiers. The ideal amplifier has a linear relationship between the input and the output, effectively multiplying the input current or voltage by some value. If this relationship is non-linear, some inputs will be multiplied more than others, and the signal will become distorted. This distortion can garble speech, and — on a wire carrying multiple telephone calls — can create cross-talk, with speech from one telephone call being heard on another.

The vacuum tube was a superior amplifier to anything that preceded it, but it wasn’t a perfectly linear amplifier; its amplification curve formed more of an S-shape, under-amplifying low values and over-amplifying high ones. For a line carrying a single telephone call, the resulting distortion could be mitigated by restricting inputs to the linear portion of the curve, but as AT&T adopted carrier modulation — carrying multiple calls at different frequencies on a single line — distortion became more of a problem.

In 1921, Harold Black, a 23 year old electrical engineer, joined AT&T. He soon produced a report analyzing the future potential of a transcontinental telephone line carrying thousands of carrier-modulated conversations. At the time, carrier modulation was being used to carry at most three calls on a single line. Black’s analysis showed that such a line would require an amplifier with far less distortion than existing vacuum tubes,and Black began to work on developing an improved amplifier as a side project.

At first, Black simply tried to create vacuum tubes with less distortion, a project that many others at AT&T were also working on. The efforts of Black and others produced higher-quality vacuum tubes, but nothing Black tried reduced the distortion to the degree he was aiming for.

After two years of failure, Black decided to pivot; rather than trying again and again to build a perfectly linear amplifier, he would accept that any amplifier he made might be imperfect, and instead find a way to remove the distortion that it introduced.

Black first tried to do this by subtracting the input signal from the amplifier’s output, leaving behind just the distortion, and then amplifying that distortion and subtracting that from the output signal. This method — the “feedforward amplifier” — worked, but not well. It required having two amplifiers (one for the original signal, and one for the distortion) that needed to have very precisely matched amplification characteristics, and maintaining that alignment over a wide range of frequencies and for a long period of time proved difficult. As Black noted later:

For example, every hour on the hour —24 hours a day —somebody had to adjust the filament current to its correct value. In doing this, they were permitting plus or minus 1/2-to-l dB variation in amplifier gain, whereas, for my purpose, the gain had to be absolutely perfect. In addition, every six hours it became necessary to adjust the B battery voltage, because the amplifier gain would be out of hand. There were other complications too, but these were enough! Nothing came of my efforts, however, because every circuit I devised turned out to be far too complex to be practical.

Black grappled with the problem over the next several years, until suddenly realizing the solution while taking the ferry to work one morning in 1927. An electromagnetic signal consisted of a wave, alternating back and forth between positive and negative voltage. If you took a fraction of the output from an amplifier, and subtracted that from the input signal before it got amplified — by modifying the input with negative feedback — you would cancel out the distortion. Because this would reduce the strength of the input signal, this would have the downside of greatly reducing the how much the signal would be amplified (known as the gain), but that was ok; the distortion would be reduced so much that you could get as much gain as you needed by stringing several such amplifiers together. And unlike Black’s feedforward amplifier, this negative feedback amplifier would be self-correcting: any unexpected change to the gain in the amplifier would become a change in the feedback signal, compensating for the change.

By the end of the year, Black had built a negative feedback amplifier that reduced distortion by a factor of 100,000. But Black had a difficult time convincing others of the merits of his invention. At the time feedback was largely considered undesirable by electrical engineers. Feedback could cause an amplifier to “sing” and start generating its own output, known as self-oscillation, overwhelming the input signal. (Think of the high-pitched sound that you get when placing a microphone next to a speaker.) Engineers went to significant efforts to prevent feedback-related problems.

At the time it was also believed that an amplifier with high levels of feedback would be fundamentally unstable. Opposition to Black’s amplifier was so severe that securing a US patent required “long drawn-out arguments with the patent office,” and the British patent office treated the invention the way they treated perpetual motion machines, demanding a working model. Harold Arnold, who had since become director of research at Bell Labs, “refused to accept a negative feedback amplifier, and directed Black to design conventional amplifiers instead.”

In practice, keeping the amplifier stable (avoiding self-oscillation) while also stringing together amplifiers in sequence proved to be a complex problem. These issues were resolved in part thanks to the help of two other Bell Labs researchers, Harry Nyquist and Henrik Bode. Nyquist and Bode studied the behavior of the negative feedback amplifier, and created mathematical tools for analyzing it and determining the conditions under which it would be stable.

Taken together, these efforts turned Black’s invention into a mainstay of electronics. Within 25 years, thanks to the work of Black, Nyquist, Bode, and others, the principle of negative feedback was “applied almost universally to amplifiers used for any purpose,” and it continues to be used in the design of modern amplifiers.

The effects of the Bell Labs work on negative feedback resonated far beyond amplification. A parallel tradition of feedback-based devices existed in mechanical engineering, in the design of things like governors and servomechanisms. During WWII, these two traditions began to merge into the modern discipline of control theory, which studies how to control a dynamic system using feedback. Feedback loops designed using control theory methods are the foundation of virtually every sort of automated system: aircraft autopilots, robotic arms, chemical plants, and the entire electrical grid all use feedback-based control loops, and the tools that Black, Nyquist, Bode, and others created to analyze the negative feedback amplifier are still used by control engineers around the world today.

The transistor

Even with negative feedback reducing their distortion, vacuum tubes were still far from ideal amplifiers. A vacuum tube is essentially a highly modified lightbulb, and has similar drawbacks as early lightbulbs: heating the filaments consumed a lot of power, and over time the tubes would burn out and need to be replaced. Mervin Kelly, a Bell Labs physicist who was promoted to director of research in 1936, wanted to replace the vacuum tube amplifiers and mechanical relays in the Bell System with solid-state devices, devices whose switching or amplification was done by way of electrons moving through a solid chunk of matter. Shortly after his promotion, Kelly began to hire physicists who were familiar with the then-novel physics of quantum mechanics, and could help better understand the behavior of solid matter. In 1938 Kelly created a group specifically devoted to solid-state research, and the team began working on building a solid-state amplifier using semiconductors: materials such as silicon, germanium, and copper oxide whose conductivity could be greatly varied. The group made progress — most notably, in 1939 Russell Ohl discovered that small amounts of impurities in silicon could drastically affect silicon’s conductivity when he accidentally created a photovoltaic cell in a silicon rod — but the work was soon disrupted by wartime priorities.

As the war progressed, Kelly recognized that wartime scientific and technological advances had the potential to upend the communications industry, and that if AT&T wanted to stay on top it needed to master the relevant sciences. He conceived of a new, major research program into solid-state physics that would be led by William Shockley, a physicist Bell Labs had hired in 1936 and who since 1938 had been researching solid-state devices.

During the war Shockley had worked on a variety of problems unrelated to semiconductors, including designing tactics for submarine warfare and training B-29 crews to use radar bombsights. But he also found time to work on a solid-state amplifier. In April of 1945, a few months before the end of the war, Shockley began to sketch out a device made from doped silicon (silicon with small amounts of impurities). Shockley hoped to use external electric fields to modify the conductivity of the silicon, amplifying the current flowing through it. His initial experiments, however, were met with failure.

A few months later, Kelly reorganized the research program at Bell Labs, creating a new, larger group wholly devoted to solid-state physics and led by Shockley. Among those who joined the new group were Walter Brattain, a physicist who had joined Bell Labs in 1929 and had previously studied copper oxide semiconductors, and John Bardeen, a new researcher from the Naval Ordnance Laboratory. Both were part of a subteam of the new solid-state physics group devoted specifically to the study of semiconductors.

Shortly after Bardeen joined, Shockley asked him to check Shockley’s calculations for the silicon amplifier in the hopes of learning why it hadn’t worked. Bardeen studied Shockley’s work, and eventually theorized that electrons might be getting “trapped” on the surface of the material, preventing the electric field from penetrating it and thus altering its conductivity. The semiconductor group began studying these “surface states,” and after nearly two years of experiments a breakthrough occurred.

Brattain had been studying surface states in silicon and germanium by shining light on the materials; if surface states existed, the light would knock away some electrons via the photovoltaic effect, “ripping holes in the semiconductor fabric.” Brattain discovered that not only was this electron disruption taking place, but that by varying the charge in an electrode above the surface of a piece of silicon, the strength of the photovoltaic effect could be varied significantly. The surface states themselves could be manipulated.

On November 21st, 1947 — a few days after Brattain’s discovery — Bardeen suggested using this ability to manipulate surface states to build an amplifier. They placed a sharp metal point onto the surface of a piece of doped silicon, and surrounded it with an electrolyte. By placing a small wire into the electrolyte and varying its voltage, they believed they could alter the conductivity of the silicon, and thus how much current flowed through it from the metal point.

The experiment worked: applying a voltage to the electrolyte boosted the current flowing through the metal point by about 10%. When Brattain rode home that night, he told the other members of his carpool that he’d “taken part in the most important experiment that I’d ever do in my life.”

Over the next several weeks, Bardeen and Brattain iterated on their new silicon amplifier. Initially its performance was poor, amplifying current only marginally, not amplifying voltage at all, and only functioning at very low electrical frequencies. But they eventually found that replacing the silicon with germanium allowed the device to amplify both current and voltage, and removing the electrolyte allowed it to amplify voltage over a wide range of frequencies.

By the middle of December, Bardeen and Brattain were ready to apply what they had learned. They fashioned a new device consisting of two thin pieces of gold foil attached to the surface of a piece of doped germanium, separated by only a 500th of an inch. They connected one wire to each of the pieces of gold foil, and a third to the piece of germanium. They reasoned that varying the current in one of the gold foil wires should amplify the current flowing through the other gold wire.

It worked: both current and voltage could be amplified across a wide range of frequencies using the device. Bardeen and Brattain had fashioned a solid-state amplifier.

By early 1948, Bell Labs had fabricated nearly a hundred copies of Bardeen and Brattain’s amplifier (which would soon be named the transistor), and was testing how it could be used in various electronic devices. By the end of May, Bell Labs engineers had built a transistor-based telephone repeater. At a June 30th press conference, Bell Labs announced the transistor to the world:

We have called it the Transistor, T-R-A-N-S-I-S-T-O-R, because it is a resistor or semiconductor device which can amplify electrical signals as they are transferred through it from input to output terminals. It is, if you will, the electrical equivalent of a vacuum tube amplifier. But there the similarity ceases. It has no vacuum, no filament, no glass tube. It is composed entirely of cold, solid substances.

The rest, of course, is history.

Bell Labs worked out how to make the transistor more reliable, and by 1949 was manufacturing them by the thousands. William Shockley, irritated at not having taken part in Bardeen and Brattain’s discovery, worked to design an alternative semiconductor amplifier, the junction transistor. Shockley’s junction transistor, first successfully fabricated by Bell Labs physicists Gordon Teal and Morgan Sparks in 1950, was far more reliable than the point-contact transistor, and it would go on to be the most widely-used transistor until the MOSFET appeared in the 1960s. In 1956 Shockley, Bardeen, and Brattain would share the Nobel Prize in physics for the discovery of the “transistor effect,” by which time Shockley had left Bell Labs to found his own semiconductor company, Shockley Semiconductor Lab. In 1957, eight disgruntled employees would leave Shockley’s company to found Fairchild Semiconductor. Departing Fairchild employees in turn went on to found their own semiconductor companies, and the so-called “Fairchildren” (which include Intel and AMD) became the foundation of Silicon Valley. The world would never be the same.

The laser

As Shockley, Bardeen and Brattain were experimenting with semiconductors in the 1940s, another Bell Labs physicist, Charles Townes, was studying microwaves. Since the invention of radio in the late 19th century, the technology had progressed by steadily marching up the electromagnetic spectrum, finding ways to use shorter and shorter wavelengths. There were a variety of reasons for this: shorter wavelengths carried more information, there were limits to how many users could occupy a particular part of the electromagnetic spectrum, antennas for shorter wavelengths could be smaller, and radars with shorter wavelengths could resolve more detail. The shortest wavelengths in use in the 1920s were tens of meters in length, but by WWII this had fallen to centimeters in length; these were known as microwaves. During the war, at the behest of the military, Townes designed microwave radars of increasingly short wavelengths. When Townes designed a radar with a 10 centimeter wavelength, he was then asked to build a 3 centimeter one; when he built that, he was asked for a 1.25 centimeter wavelength.

But the pressure to achieve smaller and smaller wavelengths was bumping up against technological limitations. Generating and amplifying smaller and smaller wavelengths required smaller and smaller components; by the time wavelengths reached the millimeter range, the components to generate the signals became so small that manufacturing them, and putting enough power through them, became impractical. What was needed was a new way to generate and amplify radio signals that didn’t require fabricating microscopic components.

In 1948 Townes left Bell Labs for Columbia University, where he pursued research related to microwave spectroscopy. Townes hoped to use microwaves of increasingly small wavelengths, on the order of millimeters, to study the molecules and atomic nuclei. Because of his interest and expertise, and because shorter-wavelength radiation might prove valuable for radars or other military uses, Townes was asked by the Navy in 1950 to form an advisory group on millimeter wave radiation, so the Navy could keep abreast of any promising developments in the field. As there was not yet any good method of generating millimeter waves, most of the group’s efforts were focused on thinking of ways to generate them.

In April of 1951, on the morning of a day-long meeting of the committee, Townes awoke early and sat on a bench in a nearby park, and began to consider ways to generate millimeter wave radiation. Certain molecules, Townes knew, radiated energy at microwave wavelengths: perhaps instead of small electronic components, he could use molecules to generate the short millimeter waves he and everyone else were looking for?

The idea was not initially very promising. Molecules would certainly radiate after absorbing energy, but they would necessarily emit less energy than they absorbed — you couldn’t simply shine a light or send a signal through a collection of molecules, and get a stronger signal back. Heated molecules would radiate energy, but to generate microwaves they would need to be so hot that they would break apart into individual atoms.

Townes did know of one way to get a molecule to boost an electromagnetic signal: stimulated emission. Normally, when an atom or molecule is struck by a photon, its energy level will be raised. Later, when it falls back to its normal state, it will emit a photon. However, if it’s struck by a photon while it’s already in an elevated energy state, it will emit a second photon of the exact same frequency: one photon becomes two.

Most molecules are usually in low-energy states, not high energy states, which means that any amplification would quickly peter out as excess photons were absorbed. But Townes realized that if a collection of molecules could be coaxed so that most of them were in high energy states — known as a “population inversion” — a cascade of stimulated emission could take place. One photon striking a high energy molecule would become two; each of those could strike another high energy molecule, becoming four, then becoming eight, then becoming 16. A small amount of electromagnetic radiation could be amplified enormously. With enough molecules in an elevated state, Townes realized that in principle there would be “no limit to the amount of energy obtainable.”

Sitting in the park, Townes took an envelope from his pocket, and worked through how such a device might work. By feeding a stream of appropriately stimulated molecules into a resonator (a cavity that would reflect electromagnetic radiation of the appropriate wavelength), any electromagnetic radiation generated within the resonator would bounce back and forth, gaining energy each time it passed through the molecules as more and more photons were generated. The device would amplify any incoming signal as an electromagnetic wave passed through the population inversion molecules. And because the resonator would reflect the wave back and forth through the molecules (a form of feedback), it could also act as an oscillator — a generator of electromagnetic signals. Power would only be limited by how quickly molecules carried energy into the resonator.

A few months later Townes assigned Jim Gordon, one of his graduate students, the task of building the device, assisted by another graduate student, Herb Zeiger. Over the next several years Gordon worked diligently to realize the ambitious concept. Many physicists considered the idea unpromising: at one point, several years into the project, the current and former heads of Townes’s department, Isidor Rabi and Polykarp Kusch, exasperatedly stated to Townes that “you should stop the work you are doing. It isn’t going to work. You know it’s not going to work. We know it’s not going to work. You’re wasting money. Just stop!” But finally in 1954, a few months after Kusch insisted the device wouldn’t work, Gordon succeeded, demonstrating both amplification and oscillation in a collection of stimulated ammonia molecules. They called their device the maser, an acronym describing the device’s Microwave Amplification by Stimulated Emission of Radiation.

The maser, once developed, eventually proved to be “the world’s most sensitive radio amplifier,” an order of magnitude more sensitive than existing microwave amplifiers. Bell Labs, which hired Gordon in 1955, used masers to amplify the signals from its Echo and Telstar satellites launched in the early 1960s. The maser also found use in radio telescopes for astronomy. But the maser had drawbacks — most notably, it had to be cooled to a few degrees above absolute zero — and it was gradually relegated to increasingly niche uses as other low-noise amplifiers were developed.

The biggest impact of the maser was likely the invention that it inspired. In 1957, Charles Townes, still attuned to the problem of generating shorter and shorter electromagnetic waves, began considering how the maser might be adapted to such a task. Historically, radio had advanced into shorter wavelengths gradually, one step at a time. But Townes realized that with the maser it might be as easy, or perhaps even easier, to skip from microwave wavelengths (roughly 1 meter to 1 millimeter) all the way down to infrared or even optical wavelengths (roughly 0.000000380 to 0.000000750 meters). By this time, Townes had re-joined Bell Labs as a part-time consultant, and Townes and another Bell Labs physicist, Art Schawlow, worked through the physics of what they referred to as an “optical maser.” In 1958, they published a paper outlining the idea.

After Townes and Schawlow’s paper was published, the race was on to build the optical maser, which soon began to be referred to as a laser, for Light Amplification by Stimulated Emission of Radiation). The first successful laser was built by Theodore Maiman at Hughes Aircraft in 1960 using a ruby crystal, but other successes quickly followed. It was soon discovered that a wide variety of materials could be made to “lase,” and before long there were lasers made from gasses, dye, glass, semiconductors, and more. In 1964, Townes would receive the Nobel Prize in physics for his work on the “maser-laser principle.”

While the laser could amplify signals that were passed into it, it proved much more useful as an oscillator, a generator of original signals. In 1959 Art Schawlow, believing that the laser would be more useful as an oscillator than an amplifier, jokingly suggested that it be renamed the LOSER. Unlike the maser, which was limited to niche uses, the laser proved to be useful for a wide variety of tasks. Within just two years of Maiman’s demonstration, a laser was used for eye surgery. By 1968, the US Air Force was dropping laser-guided bombs in combat. By 1971, Xerox PARC researchers built the first laser printer, and in 1974 the first laser-based barcode scanners were being installed. In 1980, the first commercial fiber optics lines using semiconductor lasers were made available. It’s the lasers ability to generate coherent light — light all of the same frequency, and in the same phase — that made it possible to continue to advance up the electromagnetic spectrum, and use optical wavelength electromagnetic radiation for communication.

Conclusion

Why did these various amplifiers, so many of them deriving from work at Bell Labs, end up being so valuable?

Partly it’s because an electronic amplifier is a very useful information processing device. An amplifier can amplify an electromagnetic signal, but it can also act as an electronic switch. And if the output of an amplifier is fed back into its input, an amplifier can also act as an oscillator: a generator of electromagnetic signals. Those are all useful information processing tasks, and each time a new, better amplifier was created, it extended the kinds of information processing that could be done. So these amplifiers were valuable because electronic information processing is valuable, and these amplifiers all greatly expanded the scope of information processing.

But a broader, somewhat more abstract reason is that many important technologies often act as amplifiers in one way or another. One of the most important inventions in biology, for instance, is the polymerase chain reaction, or PCR. PCR is essentially a DNA amplifier: it makes millions of copies of DNA sequences, making it far easier to study them and enabling things like the dramatic reduction in the price of genetic sequencing. The inventor of PCR, Kary Mullis, shared the 1993 Nobel Prize in Chemistry for his invention.

Examples of important amplifiers abound, because amplifiers themselves help produce abundance. Chemical catalysts, which can be thought of as amplifiers of chemical reactions, are used in everything from catalytic converters in cars to petroleum manufacturing. Simple machines like the lever or pulley amplify force, microscopes and telescopes amplify visual details. Industrial fermentation, nuclear reactors, the printing press, the Xerox machine, fractional reserve banking — all can be thought of as a type of amplifier.

So these four amplifiers were important in part because amplifiers in general are important. Amplifiers take something useful — an electromagnetic signal, a segment of DNA, a copy of a book — and make it possible to get a lot more of it, and technologies that do that are often particularly useful themselves.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week: damage to the Ras Laffan LNG facility, housing bubble risks, North Korea’s naval production, Bezos’ $100 billion for manufacturing automation, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

War in Iran

Ras Laffan, the world’s largest LNG facility in Qatar, was extensively damaged by an Iranian missile, and production has been completely shut down. The facility is responsible for something like 20% of the world’s supply of LNG, as well as for a third of global helium supply, which is used for semiconductor manufacturing. [Bloomberg] [CNBC]

Oil shipments from the UAE’s port of Fujairah have declined by two-thirds thanks to Iranian drone attacks. [Lloyds List]

To try and address rising oil prices following the closure of the Strait Hormuz, the Trump Administration has waived the Jones Act (which requires transportation between US ports to be done by US ships) for 60 days. [Reuters] It also invoked the Defense Production Act to order oil drilling to resume off the coast of California. [LA Times]

China tries to entice Taiwan to reunify by offering it energy security in the face of Middle East oil disruptions. [Reuters] And BYD dealerships are seeing a surge of interest in EVs. [Bloomberg]

Urbanist Richard Florida wonders if the war in Iran means the end of Dubai. Making your city a haven for the global elite means it’s relatively easy for them to relocate somewhere else if things turn south. “Dubai, which sits near the Strait of Hormuz, was supposed to be safe. Instead, it has been under attack by Iran since Feb. 28. More than 260 ballistic missiles and over 1,500 drones have been detected over the United Arab Emirates; most have been intercepted, but their percussive booms have become part of the city’s soundscape. The city that had spent decades billing itself as a sleek sanctuary — luxe, apolitical, income-tax-free, floating above and apart from the fractious region around it — was suddenly no longer insulated.” [NYT]

Housing

Swiss investment bank UBS has a report on which cities are at the highest risk of having a housing bubble, which they estimate by looking at trends in home prices, rents, and average incomes. Miami occupies the number 1 spot, followed by Tokyo and Zurich. [UBS]

Wired has an article about RealToken, which aims to “democratize access to real estate investment” by selling tokens representing shares of ownership in real estate properties. Apparently this has involved buying a bunch of dilapidated Detroit real estate and not maintaining it properly. “Last summer, the City of Detroit sued RealT and its founders, alleging “hundreds of blight violations.” Dorris’ property was one of many that city inspectors declared unfit for habitation. He told me that while his previous landlord wasn’t perfect, sometimes leaving Dorris to organize repairs, his building has deteriorated markedly since RealT entered the picture. The smoke detectors are missing, and the bathtub has no hot water, inspectors found. “The only way of washing is me standing over my sink,” says Dorris. “There are rats in the downstairs, there are squirrels in the upstairs.”” [Wired]

Marginal Revolution on how Denmark avoids the mortgage lock-in problem — where when interest rates rise, homeowners are reluctant to sell their home because their new one will have a higher interest rate mortgage. The Danish system has mortgages backed by a bond, which can be bought to pay off the mortgage. When interest rates rise, the price of the bond falls, incentivizing homeowners to purchase them. [Marginal Revolution]

Last Friday the Trump Administration released an executive order aimed at removing various regulatory barriers that add to the cost of building homes. [Whitehouse]

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But talking about data centers in terms of dollars spent or power consumed is somewhat abstract: it doesn’t tell us much about the sort of capabilities of the infrastructure we’re actually building, the way that “miles of track” or “miles of highway” tells us about the scale of railroad or interstate building. I wanted to get a better understanding of what the data center buildout looks like in terms of computational power.

AI and computation

By far the biggest drivers of the AI data center buildout are scaling laws. Briefly, the more data you use to train an AI model, and the bigger and more computationally expensive that model is, the better the model performs. Making better and more powerful AI models thus demands increasing amounts of computation to train and run them, and data centers are where all that computation is done.

A common measure of AI model computing power is FLOPS, floating-point operations per second. OpenAI’s GPT-2 model took an estimated 2.3x10^21 FLOP to train, while the more advanced GPT-4 took an estimated 2.1x10^25 FLOP — almost 10,000 times as much computation as GPT-2, more than 20 trillion trillion operations.

(There is, of course, much more to computer performance than just FLOPS, but it’s a useful measure of computing power and it’s what we’ll stick with here.)

A floating-point operation is exactly what it sounds like: a mathematical operation (addition, subtraction, multiplication, division) performed on floating-point numbers. A floating-point number is a way of digitally representing fraction or decimal numbers in a computer, which stores everything as a sequence of ones and zeroes. It typically has three parts: a sign (whether the number is positive or negative), and a significand (some sequence of digits) multiplied by a base raised to an exponent (which locates the decimal point).

Different standards for encoding floating-point numbers in different amounts of memory allocate a different amount of space for each of these parts. For example, the IEEE 754 standard for floating-point arithmetic specifies a 32-bit floating-point number (the size of floating-point numbers typically used in general-purpose computers) as having 1 bit for the sign, 8 bits for the exponent, and 23 bits for the significand. This finite amount of space makes floating-point operations fundamentally limited in their precision, because the less space you allocate, the less precise your number. A 16-bit floating-point number will have less precision than a 32-bit one, which will have less precision than a 64-bit one. (This will become important later.)

So how many FLOPS can a typical AI data center achieve?

Computation in a data center is done on huge numbers of graphics processing units, or GPUs, which are specialized computers designed to perform large numbers of arithmetic operations simultaneously. (GPUs were originally designed to render graphics for things like computer gaming, and for many years Nvidia was primarily a manufacturer of computer gaming graphics cards.) One common GPU is Nvidia’s H100, which was first released in 2022 and is still one of the most popular GPUs for AI-related computing tasks. Estimates of data center capacity will often be done in terms of “H100 equivalents.” Per Epoch AI’s dataset on large GPU clusters, a typical AI data center will have around 100,000 H100 equivalents, and a very large one might have 1 million or more. Meta’s planned 5-gigawatt data center campus in Louisiana is estimated to have over 4 million H100 equivalents when it’s complete.

How much computational capacity does an H100 have?

This is where it starts to get complex. GPUs designed for AI tasks, like the H100, are able to perform more computation on less precise numbers. For a typical 32-bit floating-point number (FP32), an H100 can do 60–67 teraFLOPS depending on the configuration: up to 67 x 10^12, or 67 trillion, floating-point operations per second. But with 16-bit numbers (FP16), an H100 can achieve 1,979 teraFLOPS, an increase of almost 30 times. And with 8-bit floating-point numbers (FP8), it can double that again to 3,958 teraFLOPS.

However, outside of FP32 and FP64, these performance levels are achieved with something called sparsity. Sparsity occurs when for a group of four values in a matrix, at least two of them are zero. When this occurs, the GPU can skip multiplications of the zero values, effectively cutting in half the number of operations it must perform. If the matrix isn’t sparse (if the matrix is dense), the listed performance numbers will fall by roughly half.

When training an AI model, sparsity basically can’t be achieved at all. When running a model it can be, but taking advantage of it requires putting the model through an extra step known as pruning. So only in certain cases can these published H100 performance levels actually be reached.

Most general-purpose computing is done using higher-precision FP32 floating-point numbers. But for training and running AI models, it turns out that good results can be achieved with 16-bit, 8-bit, or even 4-bit floating-point numbers.

How does the computational capacity of an H100 compare to other types of computer, say, an iPhone?

The iPhone 16 uses Apple’s A18 chip and features a six-core GPU on the Pro version. Estimates of the computational capacity of the A18 vary, but it seems to be on the order of 2–3 teraFLOPS using FP32, and perhaps double that using FP16. The A18 also has a 16-core neural processing unit (NPU) capable of 35 trillion operations per second (TOPS) with what appears to be 8-bit integers (INT8). By comparison, the H100 can do up to 3,958 TOPS at INT8 with sparsity, an increase of 113 times. (The A18 also has a CPU, but this apparently adds a negligible amount of computational capacity.)

To put this all together: an H100 has 20–30 times the computational capacity of an iPhone 16 GPU when it’s doing mathematical operations with 32-bit floating-point numbers, but around 137-275 times the capacity when working with 16-bit numbers (depending on whether you have sparsity or not). And an H100 has around 56-113 times the capacity of the A18’s NPU. If we assume that both the NPU and GPU can be used together, this suggests an H100 has on the order of 50-100 times the computational capacity of an iPhone 16.1 A typical AI data center with 100,000 H100 equivalents will be roughly equivalent to 5-10 million iPhone 16s, and a monstrous 5 GW data center will be equivalent to 200-400 million (!) iPhone 16s.

Of course, in practice you couldn’t achieve anything like an H100 performance by wiring a bunch of iPhones together; the H100 is designed to be connected to thousands of other H100s, and has massive interconnect and memory bandwidth to make that possible, which the iPhone doesn’t. But this gives us a rough idea of the computational capacities involved.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week we look at closure of the Strait of Hormuz, banning build-to-rent homes in the US, Honda’s EV losses, Travis Kalanick’s new company, Corpus Christi’s water crisis, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housekeeping items this week:

• I added an extra section on the infrastructure-related issues of the war in Iran this week. The normal reading list continues below the paywall.

• I was a guest on the Go/No-go podcast.

War in Iran

Iran has begun to attack ships passing through the Strait of Hormuz; in normal times the strait carries about 3000 ships per month, including a large number of oil tankers. Roughly 20% of the world’s oil — around 20 million barrels of oil per day — passes through the Strait of Hormuz. This has effectively shut down traffic through the strait, because insurance companies have withdrawn coverage. Via Shanaka Anslem Perera on Substack:

At midnight Greenwich Mean Time on 5 March 2026, seven of the twelve International Group Protection and Indemnity clubs that collectively insure roughly 90% of the world’s ocean-going tonnage executed identical cancellation notices for war-risk coverage across the Persian Gulf, the Gulf of Oman, and Iranian territorial waters.

Gard, NorthStandard, Skuld, Steamship Mutual, the American Club, the Swedish Club, and the London P&I Club withdrew coverage. They did not act because a government ordered them to. They did not act because a naval commander declared a blockade. They did not act because a single mine had been laid in the shipping channel. They withdrew because their London treaty reinsurers, confronting unlimited tail exposure in an active combat zone, could no longer satisfy the 99.5% Value-at-Risk capital charges mandated by the European Union’s Solvency II directive. The reinsurers pulled capacity. The clubs, which operate as mutuals whose losses fall directly on member shipowners, had no mathematical alternative.

And from NBC:

Even in peacetime, the world of shipping is a bureaucratic labyrinth of captains, owners, brokers and insurers. When war breaks out, many insurers trigger what are known as standard war-risk cancellation clauses, according to Jungman at Vortexa.

These clauses allow insurers to “withdraw coverage on short notice when an area becomes an active conflict zone,” she said. And “without insurance, most commercial ships simply cannot sail.”

Some companies do provide insurance, “but the pricing and conditions can be extremely restrictive,” Jungman added. In some cases, premiums have risen by as much as 1,000%, according to Reuters.

Thanks to the disruption, oil prices have spiked, affecting the price of not just gas but lots of other products. Via Reuters:

Oil prices rose to $119 a barrel on Monday, their highest level since 2022 because of the disruption, though they dropped again before the market closed. If supply disruptions are prolonged, prices could rise further until the recessionary effect of higher energy costs destroys demand. Crude oil, gasoline, diesel, jet fuel, natural gas, petrochemicals, power, and fertilizer prices have all risen sharply since the conflict began.

As a result of the closure, various companies are declaring force majeure (extreme circumstances which frees you from liability for breaking a contract). Via Petar Momchev on Twitter:

To try and reduce oil prices, the Trump Administration plans to suspend the Jones Act, which requires goods carried between US ports to be carried on US built ships.

The 30-day exemption, which is still being developed, is set to apply broadly to vessels moving oil, gasoline, diesel, liquefied natural gas and fertilizer among US ports, the people said. That would enable generally cheaper foreign tankers to move those goods — including Gulf Coast oil to refineries on the US East Coast and fuel from the region to more populous areas.

Japan also plans to release part of its strategic petroleum reserve.

Oil isn’t the only thing that passes through the Strait of Hormuz. The Middle East is responsible for a large share of fertilizer exports (because the production of fertilizer uses natural gas as both a chemical feedstock and a source of cheap energy), and a large chunk of the world’s fertilizer passes through the Strait. Fertilizer prices have spiked. Via Carnegie Endowment for Peace:

In particular, Gulf countries are important producers of nitrogen fertilizers, which depend primarily on natural gas burned at high pressure in the presence of hydrogen to synthesize ammonia. (The hydrogen usually comes from natural gas as well.)

But it’s not just that Gulf fertilizer can’t make it to export markets such as Sudan, Brazil, or Sri Lanka. It’s also that fertilizer producers elsewhere lack key ingredients. This is where the second-order effects of a supply chain crisis appear, just as they did during Russia’s invasion of Ukraine in 2022, which sent fertilizer prices soaring.

Deprived of their natural gas supplies from Qatar, fertilizer firms in India, Bangladesh, and Pakistan have had to shut down production.

The war might damage the extensive desalination infrastructure that countries in the middle east rely on to produce fresh water. Via the Associated Press:

The war that began Feb. 28 with U.S. and Israeli attacks on Iran has already brought fighting close to key desalination infrastructure. On March 2, Iranian strikes on Dubai’s Jebel Ali port landed some 12 miles from one of the world’s largest desalination plants, which produces much of the city’s drinking water.

And attacks on Tehran’s oil facilities have created a poisonous “black rain.” Via the BBC:

Since the US-Israeli attacks on Iran began on 28 February, we have confirmed strikes on at least four oil facilities around the capital.

Residents said smog and pollution have blocked out the Sun and left a strong smell of burning in parts of the city, while experts warn the scale of some of the pollutants released could be “unprecedented”.

The spike in air pollution appears to focus near the damaged oil sites around the capital - a city with a population of nearly 10 million, with millions more in the surrounding areas.

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The concept of industrializing homebuilding by bringing the process into the factory began to be articulated almost as soon as the benefits of mass production became apparent. Around the 1920s Alfred P. Sloan, president of General Motors, extolled the virtues of industrialized production, noting that an $800 Chevrolet would cost $5000 if it were made by hand, and suggested that the costs of building a home could be similarly and dramatically reduced using factory methods. In 1928, the German architect Walter Gropius noted that between 1913 and 1926 the price of new cars had halved, while the price of construction had doubled. Gropius later attributed this to the different production methods of car manufacturing and homebuilding, declaring that contemporary building methods were “far behind the times” and “not fit to solve the problem” of building affordable homes:

The greater proportion of hand-work involved in building increased the price in accordance with the increasing labor costs. Refinement of mass production methods, on the other hand, considerably lowered the price of automobiles. A decent dwelling became unattainable for the poor, yet the car became an everyman’s tool.

The potential efficiency gains and cost savings of factory-based construction have been a driver of numerous prefabricated — factory-built — homebuilding efforts. They were behind the Lustron Corporation, which received $37.5 million (over $500 million in 2026 dollars) in government funding to produce an enameled-steel panel home in an enormous former aircraft engine factory following WWII. They fuelled Operation Breakthrough, a 1970s US government initiative to kickstart the industrialized production of housing. They formed the core thesis of Katerra, a construction startup that in 2018 raised over $2 billion in venture capital on promises of driving down the costs of construction with factory methods (disclosure: I formerly managed an engineering team at Katerra).

However, these hopes have yet to bear out, and achieving cost savings with prefabricated construction has proved to be highly elusive in practice. Factory-based building methods have been tried extensively both in the US and abroad, but it’s hard to find examples of prefabricators achieving significant cost savings above more traditional methods. The savings that have occurred are frequently in the realm of 10-20%, a far cry from the huge reductions that followed the industrialization of car manufacturing. Often these cost savings don’t materialize at all, and prefabricators instead emphasize other benefits of factory methods like reduced construction time and increased quality. In cases where major savings do occur — such as with mobile homes — it’s often within somewhat narrow categories of building that have not generalized to the broad construction market.

What should we expect from factory-based homebuilding?

Before we look at the history of cost savings with prefabricated construction, it’s worth articulating what, specifically, we might hope to gain by using factory-based construction methods.

Prior to the age of mass-production, cars were assembled using craft production methods. In “The Machine that Changed the World”, a study of Japanese car manufacturing methods, the authors describe how cars were assembled at Panhard et Levassor, a French machine-tool company which at the end of the 19th century was the world’s leading car manufacturer:

P&L’s workforce was overwhelmingly composed of skilled craftspeople who carefully hand-built cars in small numbers…different contractors, using slightly different gauges, made the parts. They then ran the parts through an oven to harden their surfaces enough to withstand heavy use. However, the parts frequently warped in the oven and needed further machining to regain their original shape.

When these parts eventually arrived at P&L’s final assembly hall, their specifications could best be described as approximate. The job of the skilled fitters in the hall was to take the first two parts and file them down until they fit perfectly. Then they filed the third part until it fit the first two, and so on until the whole vehicle — with its hundreds of parts — was complete…by the time the fitters reached the last part, the total vehicle could differ significantly in dimensions from the car on the next stand that was being built to the same blueprints.

Because P&L couldn’t mass-produce identical cars, it didn’t try. Instead, it concentrated on tailoring each product to the precise desires of individual buyers.

This was a time-consuming and labor intensive process, and cars produced in this manner were expensive: in the early 1900s a new car cost on the order of $2000 to $3000 ($77,000 to $116,000 today).

Henry Ford and his systems of mass production changed all that. By introducing a series of manufacturing improvements — machine-made interchangeable parts, the moving assembly line, special-purpose automated machine tools — Ford was able to dramatically reduce the cost of producing a car. In 1908, Ford’s Model T cost $850, far less than competing cars. And as production methods improved and manufacturing scale increased, the costs fell even further: by 1925, a Model T was selling for just $260, a reduction of more than 80% in inflation-adjusted terms.

Notably, the enormous reductions in cost didn’t come at the expense of quality. An analysis of Buick’s 1911 Model 10, a competitor of the Model T, noted that “[a]nyone comparing a Model T Ford side by side with a Model 10 Buick would be unable to find anything superior on the Buick other than it had more brass trim. The Buick is crudely constructed, in essence years behind a Model T Ford in terms of manufacturing ease and serviceability, performance, and reliability. The Buick Model 10 is slow, heavy, and small.” This increase in quality meant reduced maintenance costs; the Model T cost around $100 a year to maintain at a time when other cars cost $1500.

Since automobiles transitioned from craft to industrialized production, the cost of cars has continued to fall in inflation-adjusted terms. And we see this pattern more broadly with manufactured goods: they tend to get cheaper over time. If we look at categories of manufactured goods in the consumer price index over the last several decades, nearly all of them have fallen in price in real terms.

Notably, this shift to industrialized production isn’t dependent on the success of a single firm. Ford’s share of automobile sales peaked at around 60% of the US car market in the early 1920s, but by the 1930s it had fallen behind General Motors and Dodge, and by the middle of WWII the company “was on the brink of collapse,” to the point where the government considered taking it over. But even if Ford had gone out of business, the industry wouldn’t have shifted back to craft-methods of production. Similarly, a dramatic decline in the overall car market — car sales declined by about 75% during the Great Depression, and by nearly 50% following the global financial crisis — didn’t cause a shift back towards craft methods of production. Once industrialized production arrives, is benefits ensures that it sticks around even in the face of market downturns.

The promise of factory-based construction then, is that the new methods that are so superior, and result in such great decreases in cost while offering equal or even superior quality, that going back to the old methods is unthinkable, even in the face of major firm failures or market declines. People will often articulate other benefits of prefabricated construction, and these benefits are often real and valuable, but it’s the promise of durably improved efficiency and reduced cost that has continuously inspired enthusiasm for the concept.

A brief history of prefabricated homebuilding

The history of prefabricated construction dates back hundreds of years. In 1624, English colonists brought a prefabricated panelized house with them when they arrived at Cape Ann, Massachusetts which was disassembled and reassembled several times.

During the California Gold Rush of 1848, thousands of prefab homes were exported to California from the eastern US as well as England, France, Germany, and even China: by 1850, 5000 homes had been ordered and shipped to California from the New York area alone. In 1861, the lumber dealers Skillings & Flint patented a “portable house” made of panelized wood construction which could be erected in three hours, many of which they sold to the Union Army during the Civil War. In 1892, Ernest Hodgson began to sell prefabricated cottages made from wood panels, which the company continued to sell in one form or another until the 1970s.1

But it was really in the 1930s when the idea of prefabrication as a strategy for reducing the costs of building a home began to emerge. The ongoing Depression, and the high costs of housing, had put homes out of reach for many Americans. The success of mass production, or “Fordism,” in driving down the costs of cars inspired many entrepreneurs to try a similar strategy with homebuilding. An article in the Brooklyn Daily Eagle from 1935 noted that “mass production of prefabricated housing promises to revolutionize homemaking for the average family.” One early example was the Motohome, which debuted that same year as the “prefabricated house that comes complete with food in the kitchen.” Promotional material for the Motohome noted that other goods had become cheap thanks to mass production, and it was only logical to apply such methods to homebuilding:

THE greatest social and economic problem that has grown out of the depression has been the necessity of reducing the cost of homes to a price that is not out of balance with the price you pay for other necessities of life. Home building for years has remained on an antiquated basis. That is why the cost of homes has been going steadily higher and higher in relation to the cost of other things that have been lowered through the aid of mass production. It is obvious that by manufacturing homes on a mass-production basis, the cost can be brought down to a point where you may again afford to own your home and give your children their chance in life.

The Motohome was not particularly successful, selling only 150 units, and subsequent home designs from its manufacturer American Houses reduced the extent of prefabrication to “precutting [material] and partial preassembly of panels.”

But other prefab companies found more success. In 1934, Foster Gunnison, a salesman of lighting fixtures, founded “Gunnison Magic Homes” (later shortened to Gunnison Homes), which aimed to “bring the full benefits of mass production technology to a backwards industry.” Gunnison hired manufacturing experts from car manufacturing, and developed a system of panelized construction using stressed-skin plywood construction (where the plywood exterior carries the weight of the structure, rather than wood studs) first developed by the US Forest Products Laboratories. Gunnison aimed to make his company “the General Motors of the homebuilding field,” and by 1944 Gunnison Homes’ factories were producing 600 houses a month. And in 1940, brothers George and James Price founded National Homes, producing prefabricated wood panel homes in their factory in Lafayette, Indiana. Within two years, National Homes had produced over 3600 homes. Overall, between 1935 and 1940 prefabricators produced an estimated 10,000 homes in the US.

Prefabricated construction gained further traction during WWII, when it was widely used to rapidly build homes for war workers moving into defense production areas. The 1942 Lanham Act authorized five manufacturers to produce 70,000 prefab homes for $153 million, and by 1943 there were at least 20 firms which had each manufactured more than 1000 houses, and several firms which were producing several hundred prefab homes a month. By the end of the war an estimated 200,000 prefabricated homes were built, roughly 12.5% of the 1.6 million homes built in America during the war.

Following the war, US housing expediter Wilson Wyatt planned to use prefabrication to rapidly build large numbers of homes to address an “acute housing shortage”: 250,000 prefab houses were planned for 1946, and 600,000 for 1947. This didn’t come to pass — Wyatt resigned after less than a year, and the actual number of prefabs built in 1947 was around 25-35,000. But by the 1950s prefab construction was nevertheless beginning to carve out a meaningful, and increasing, fraction of the US housing market. In 1954 prefabs were around 5-8% of total single family home construction; by 1959 that fraction had risen to 6-12%.

Many of these prefab homes were mobile homes: permanently occupied travel trailers. But prefabricated construction of conventional homes was also becoming more popular. National Homes, in particular, became a force to be reckoned with in the prefab industry. In 1953 National Homes produced just over 14,000 homes, roughly 2% of all US houses built. In 1959, the company merged with seven other prefabricators, giving it a network of nine plants across the US (some of which it was eventually forced to divest thanks to an anti-trust lawsuit). In 1956 the company had produced its 100,000th home; by 1960, it had hit 200,000, and by 1963 a quarter of a million. Founder James Price had the audacious goal of being responsible for 50% of ALL US housing starts by 1975.

During the 1960s mobile homes, today known as manufactured homes, also began to rapidly gain in popularity. Mobile home sales rose from 90,000 in 1960 to nearly 600,000 in 1972, at which point there were mobile home factories in 45 states. By 1974, Skyline, the largest mobile home manufacturer in the US, was producing 50,000 homes a year, more than any other homebuilder, and the Mobile Home Manufacturer’s Association had become the world’s largest land developer as part of its efforts to build new mobile home parks. By the early 1970s, mobile homes along with other types of prefabricated, factory-built construction made up on the order of half (and possibly more) of all single family home construction.

But the early 1970s proved to be the peak of factory-built housing in the US. During the 1970s, many prefab firms went out of business or otherwise struggled. In 1972 the largest prefab factory in the US, owned by Behring Corporation, closed. Gunnison Homes, which had been purchased by US Steel in 1944, stopped producing homes in 1974. EL Hogdson similarly went out of business in the 1970s. National Homes entered a period of decline, losing $10 million in 1973 and $20 million in 1974, though the company managed to remain in business until the 1980s. Operation Breakthrough, a major government effort to kickstart large-scale production of factory-built housing, failed; despite $72 million in investment (plus millions more in Section 236 financing), 20 of the 22 building systems funded by Breakthrough were no longer being produced by the 1980s. Between 1973 and 1976 the number of mobile home factories in the US declined by nearly 40%, and the number of wood panel home factories declined by nearly 60%.

Since the 1970s, prefab has continued to gradually lose ground in the US housing market. Cardinal Industries, which was the largest prefab homebuilder in the US following the demise of National Homes, declared bankruptcy in 1989. By 1990, mobile homes (which had since been confusingly renamed “manufactured homes”) and other prefabs together were down to around 20% of the US single family home market, and almost certainly a much smaller share of the multifamily apartment market. Manufactured homes saw an uptick in popularity through the 1990s, buoyed by lax lending standards, but sales collapsed in the early 2000s as lending standards tightened and repossessed homes flooded the market. Other types of prefabricated homes declined significantly following the 2008 Global Financial Crisis.2

In the 2010s, there was a raft of US startups which spent enormous amounts of money to try and bring factory-based methods to homebuilding. In addition to Katerra ($2 billion), there was Veev ($475 million), Blu Homes ($217 million), Mighty Buildings ($100 million), Entekra ($75 million), and others. These companies all went out of business or otherwise failed (Mighty Buildings, for instance, pivoted to selling exterior cladding systems).

Today, prefabricated construction remains a small fraction of overall homebuilding in the US. For single family homes it’s around 10%, most of which are manufactured homes. For multifamily apartments, it’s roughly 3%.

Thus the lack of a robust prefab housing construction industry in the US is not for lack of trying. Many firms have taken swings at being the “Henry Ford” of housing: often these firms have been backed by very substantial investments, and many of them have produced thousands of homes. But none of them have catalyzed a transformation of the American homebuilding industry the way that Ford did for the car industry.

Prefab outside the US

Prefabricated home construction has a similarly long history of use outside the US. In the UK, following WWII the government funded the construction of over 150,000 prefabricated homes as part of its “Emergency Factory Made” housing program. The most notable of these was the AIROH house, which was designed to make use of now-surplus aircraft manufacturing plants and the 100,000 tons of scrap aluminum that no longer needed to be turned into airplanes. Over 54,000 aluminum-framed AIROH houses were produced during the program, each one using two tons of scrap aluminum.

More recently, the UK government has poured enormous amounts of investment to encourage the adoption of prefabricated construction (what they refer to as “Modern Methods of Construction” or MMC). As in the US, this effort has been marked by numerous high-profile failures and bankruptcies. Tophat, a modular builder formed in 2016, secured over £160 million (~$215 million) in funding, then operated at a loss between 2020 and 2023 before declaring bankruptcy. Legal & General, a financial services company, formed a prefab homebuilding division in 2016; by the time it closed in 2023, it had lost £236 million (~$316 million). Ilke Homes, a modular homebuilder founded in 2017, closed in 2023 owning £320 million (~$429 million) in debt. Today, prefab construction remains a small fraction of the UK’s homebuilding industry, around 5-10%.

In Germany, prefab construction has had more success. Millions of precast concrete apartments were built in East Germany during the Cold War, and since the 1990s prefabricated construction (much of it using wood-panel construction) has gradually become more popular. From less than 10% of new housing units in 1990, prefab today accounts for more than 25% of new German homes.

Japan has similarly built large numbers of prefabricated homes. Prefabricated construction began to gain popularity in the 1960s, and by the 1970s Japan was producing over 100,000 prefabricated homes annually. Since the 1960s more than 10 million prefab homes have been built in Japan, and prefab makes up roughly 15% of the Japanese housing market.

But the country that has adopted prefabrication more than any other country is Sweden. Roughly 85% of single family homes, and 30-40% of multifamily apartments in Sweden are prefabricated.

It’s clear, then, that factory-based construction methods have been tried extensively, both in the US and around the world. It’s also equally clear that, in general, these methods have not displaced conventional construction. Outside of a few Scandinavian countries, prefabrication does not appear to be the primary method of homebuilding used in any major country.

Prefab’s record of cost savings

One major reason for the relative lack of success of prefab construction is cost. Unlike car manufacturing, where industrialized methods rapidly and dramatically reduced the cost to manufacture a car, the cost savings yielded by prefabricated construction have historically been much more modest, if indeed they’ve occurred at all.

The chart below shows the price per square foot of prefabricated homes built by different US manufacturers in the 1950s. It also includes the price per square foot of a typical conventionally-built US home in the 1950s.

And this chart shows the price per square foot of several prefabricated homes (not including land) circa 1947.

We can see that most prefabricated homes cost roughly as much, or more, than conventional construction. Only six of the 22 1950s homes have a lower square-foot cost than conventional construction, and none of the 1947 homes are.3 And the lower costs that do exist are somewhat modest, ranging from 5-20%. What’s more, some of this cost savings comes from prefabricated homes having fewer features, rather than any sort of factory-based productivity improvement. National Homes’ 1953 catalog, for instance, notes that to keep prices low, “certain minor features come to you unfinished.” The National Homes low-cost “Cadet” model didn’t include interior paint, had a concrete or plywood floor in lieu of things like carpet or tile, had no closet doors, and had no attic or garage (though a carport could be added for an extra fee).

Skipping ahead to the late 1960s and 1970s, we see a similar pattern. The 1969 report “Building the American City” from the presidentially-appointed Douglas Commission included a section on factory-based construction methods. It estimated the potential savings of a fully factory-built house as being around 16.5% when compared to conventional construction. Likewise, the 1975 publication “Good Shelter: A Guide to Mobile, Modular, and Prefabricated Houses” notes that in 1972 the average prefab home cost on average $9 to $12 per square foot. This is a substantial savings of 33-50% compared to the average cost of conventional home construction (around $18 per square foot that year), but it’s likely an outlier; the same publication gives prices of $18 to $20 per square foot for 1974, only slightly less than the average conventional cost of around $21 per square foot that year. A 1977 issue of the homebuilder publication “House and Home” gives a typical prefab house cost of $15 to $17 per square foot, plus the cost of shipping. This is substantially less than the typical conventional construction cost that year (around $29 per square foot), but that’s the cost the builder pays the factory, and doesn’t include any of the necessary site work (foundations, utilities), the cost to install or finish the home, or the builder’s profit margin. Once these are added in, the cost advantage becomes more marginal: the article notes that that price allows the builder to be competitive with conventional building methods, and offer some modest cost savings (which trades off against the reduced flexibility of prefab construction).4 It states that builders choose prefab to “get houses up quickly, or to sell in remote areas where assembling crews for conventional building would be difficult, or to control costs more accurately,” not for any major cost savings.

Operation Breakthrough, an ambitious government program to industrialize US homebuilding, provides another example of limited cost savings of prefabricated construction. Breakthrough began in 1969, and funded the development and construction of 22 housing systems (out of several hundred applicants) in the hopes that some of them would eventually achieve true mass production. Several thousand Breakthrough housing units were constructed at demonstration sites around the country, and HUD financed the construction of tens of thousands more under its Section 236 program for low-income housing.

As part of the program, the housing system producers estimated the cost of the systems they were developing. Only 5 of the 22 systems estimated their costs would be less than conventional construction, and once again these estimated savings were modest, on the order of 5 to 20%. And it seems unlikely that in practice these savings were achieved: the government paid tens of millions of dollars to the demonstration site developers to cover the excess costs of the housing systems beyond their market value, and by the 1980s 20 of the 22 housing systems (including every “low cost” system) were no longer in production.

By the 1980s, US prefab homebuilders had largely given up trying to compete by offering low-cost homes, a segment which was increasingly dominated by mobile homes. From a 1981 article in House and Home:

Ten years ago, [factory-built housing’s] banner had been “affordability,” and it had cultivated the image of an industry about to spark a revolution that would stand the conventional housing industry — slogging along with supposedly antiquated building methods — on its ear.

That revolution never came…

The sentiments expressed by John Baxter, manager of operations for Boise Cascade, are typical of how the industry sees itself now. “Modular housing is not going to be the answer for affordable housing in the next ten years,” says Baxter. [National Association of Home Manufacturers’ John] Kupferer agrees, and gladly passes the affordability banner on: “Mobile homes really have the answer when it comes to affordable housing.” And instead of that revolution, Kupferer sees modular’s share of the housing market growing via evolution.

The thrust of this evolution, says Kupferer, is that “dollar for dollar, you get much more value in a manufactured house.” There will be no more trying to stress a lower cost-per-square-foot than conventional builders: “We never say we can beat the other guy’s price. We don’t want to be known as the cheaper house.” (house and home march 1981).

The trends of prefabricated construction offering modest, if any, cost savings continued into the 1990s. A 1991 article on Utah prefab homebuilder Valgardson Housing System gives its costs per square foot as around $44, around a 19% savings compared to average conventional construction costs that year (deseret news, census). A 1998 HUD/NAHB study comparing conventional and factory-built homes found that a factory-built house would cost around 15% less than an equivalent conventional, site-built house.

This state of affairs continues today. Successful prefab homebuilders in the US rarely tout their cost savings, and instead emphasize benefits like faster build time or higher quality. High-end prefab homebuilder Bensonwood states on their website that “[a]pples to apples, we build a more consistent, higher quality product for less money than can be built with conventional construction methods. We are not, however, price-competitive with stick-built homes built to code-minimum levels.“ Vaughan Buckley, the CEO of modular multifamily builder Volumetric Building Companies, noted that cost savings of prefab was on the order of 5 to 10%, and that “It’s not a huge swing in cost that our clients typically seek modular for. It’s the reduction in schedule.” The founder of prefab design firm CleverHomes stated that “while there can be some savings with modular prefab construction, the more substantive benefits are the predictability of budget and process, the quality control, the speed of construction.” Derrick Seitz of construction management firm Windover construction stated that “[o]ne thing we always make clear at the forefront with clients considering modular is that the construction costs are the same as conventional methods.”

Outside of the US, we see the same thing. Sweden, as we’ve noted, has adopted prefabricated construction more than almost any other country; 85% of single family homes, and 40-45% of multifamily apartments in Sweden are prefabricated. But this hasn’t yielded low housing costs. Per Statistics Sweden, in 2023 the average price per square foot for a new single family home in Sweden was more than 70% higher than the average price in the US. And over the last 25 years construction costs and housing prices in Sweden have risen roughly as fast as they have in the US and above the level of overall inflation, rather than less than inflation like other manufactured goods.

In a New York Times article about Swedish prefabrication, Stefan Lindbäck, an executive at Swedish multifamily prefab company Lindbäcks admits that their construction costs aren’t lower than conventional builders, but suggests that they make up for it in other ways:

Building quality homes, whether on-site or off-site, will never be cheap. You don’t want to scrimp on materials or labor, and the savings of factory-built homes might not be obvious at the start, Lindbäck told our group. A conventional builder might bid lower than Lindbäcks, but then there are the costs of supervising the construction on-site and paying for delays in interest charges. And conventional builders profit from changes late in the process.

With factory-built houses, modifications are minimized because customers generally select from a standardized framework and changes are allowed only up to a certain point. The factory builder’s advantage is quality control and speed. Real profit, long-term profit, comes from streamlining the building system for predictable outcomes and fast delivery.

Similarly, Germany’s prefab home portal Fertighaus.de notes that “which construction method is cheaper, prefabricated house or solid house [on-site concrete or masonry construction], then the answer for an equivalent design is: None.” German homebuilding platform Massivehaus.de asks: “Is a prefabricated house cheaper than a solid house? In short: no. Prefabricated house prices as well as those of solid houses exist in a wide variety of levels, and none of the construction methods can be assessed as cheaper or more expensive today.”

German prefab homebuilder HUF HAUS likewise lists the advantages of choosing prefab construction, but these advantages do not include “lower price;” instead they are things like short construction time, energy efficiency, and a guaranteed fixed price.

In the UK, the CEO of prefab builder Vision Modular noted in 2023 that what drove demand for factory-built construction methods was “certainty of cost, of programme and of quality”, as well as sustainability and ESG requirements — not any cost savings. The numerous failures of various UK factory-built housing producers likewise suggests an inability to achieve any sort of major (if any) reduction in building costs. When the Swan Housing Association closed its factory in 2022 after five years of operation, it noted that “it is simply not financially sustainable to continue to build homes using modular construction, with Swan’s factory having been running at a loss.”

In Japan, the trajectory of prefabricated construction mirrors that of the US: an initial focus on using factory methods to achieve low costs, followed by a pivot to focusing on higher quality when that strategy failed. A 2003 study of Japanese prefabricated construction noted that:

During the 1960s and 1970s, Japanese housing manufacturers focused solely on the ‘mass production’ of their products, resulting in a supply of virtually identical, rather monotonous houses. Due to the ‘inferior’ image associated with the low-quality appearance of these houses, the public immediately rejected them. Since then, manufacturers have placed greater emphasis on improving industrialized housing quality…

The same paper notes that as of 2000 per-square foot costs of prefabricated construction in Japan were on average 8% higher than site-built construction. Likewise, the Japanese homebuilding platform japanese-architects.com notes that the least expensive method of building a new home is typically local, on-site builders, rather than prefabricated construction.

Toyota is an illustrative Japanese case. In the 1970s, in an attempt to apply its manufacturing expertise to other industries, Toyota formed a prefabricated homebuilding division, Toyota Home, which still exists today. But while Toyota’s manufacturing expertise, and its low-cost, high-quality cars, allowed it to become the largest automaker in the world, it hasn’t had the same success in homebuilding. Toyota Home has consistently been a minor homebuilder in Japan, and like other prefabricators it seems more focused on delivering high quality than on achieving low price (unlike with car manufacturing, where it successfully achieved both). In 2008 Toyota’s home prices ranged from $200 to $300 per square foot, far more than average prices in the US that year (around $90 per square foot per the US Census).

When there is cost savings, it’s often because a prefabricator can locate their operation in a place with low-cost labor, and ship it to a place with high-cost labor. Modular manufacturer Autovol is able to manufacture modules in their Utah factory (where labor costs are low), and ship them to California (where on-site labor costs would be high). Stack Modular uses a similar business model, manufacturing modules in China and shipping them to the western US and Canada. But this requires a substantial labor price differential, and the high costs of transporting large, bulky prefabricated building components means that there’s a limited number of places where this strategy works.

Thus wherever and whenever we look, we generally see the same story: factory-based homebuilding yielding at best relatively modest cost savings, and often no savings whatsoever. This is what we see in the US historically, it’s what we see in the US today, and it’s what we see in countries around the world.

Manufactured homes and cost savings

The biggest counterexample to this general trend that I’m aware of is manufactured homes, formerly known as mobile homes. Mobile homes were originally camping trailers, but over time have evolved to more closely resemble conventional homes, and today’s manufactured homes can be hard to distinguish from site-built homes at first glance. Unlike other types of housing in the US (including other types of prefabricated housing), manufactured homes don’t conform to local building code requirements, but instead follow a federal code authored and administered by HUD.

Unlike other types of prefabricated construction, manufactured homes have consistently been substantially cheaper than conventional construction. The cost of a “single-wide” – a manufactured home that comes in a single unit — has generally been 55-65% less per square foot than the cost of a conventional home. “Double wides” — larger manufactured homes that consist of two units stitched together — are somewhat more expensive, but still dramatically less than conventional single family homes.

Do manufactured homes show that factory-based construction can, in fact, yield substantial cost savings?

One challenge when comparing the costs of manufactured homes and conventional homes is properly adjusting for differences in quality. Historically, manufactured or mobile homes were of lower quality than conventionally built homes, both in their performance characteristics and the level of interior finish. Prior to the introduction of the HUD code in the 1970s, for instance, mobile homes were sometimes known as “ten second trailers” because of how quickly fire would spread in them. Ralph Nader’s Center for Auto Safety published a book in 1975 called “Mobile Homes: the Low Cost Housing Hoax,” documenting various deficiencies of mobile homes, such as leaky pipes, bowing walls, and faulty wiring and HVAC systems.

The quality of modern manufactured homes is far higher than it was historically, but there are still performance and finish differences in manufactured homes as compared to conventional homes. For instance, manufactured homes will often use vinyl-on-gypsum (VOG) panels with visible seams for the interior, rather than higher-quality (but more expensive) drywall, and manufactured home energy efficiency requirements are far less than conventional home construction.

Determining the extent to which factory efficiencies are behind manufactured homes low costs requires adjusting for these sorts of performance and interior finish differences, and comparing a manufactured home to a similarly-specced conventional home. This sort of comparison was done in a 2023 study by the Joint Center for Housing Studies at Harvard. The study compares the costs of a single-wide, double-wide, and conventional house which each have similar numbers of bedrooms and bathrooms, interior and exterior finishes, appliances, and HVAC systems. The study also included the costs of a CrossMod, a relatively new category of manufactured home designed to be similar enough to conventional site-built homes that it is eligible for Fannie Mae financing. CrossMods have permanent foundations, more stringent energy efficiency requirements, higher quality interior and finishes, and features like porches or garages.

Per the study, single-section manufactured homes ranged from 35-47% of the cost of a conventional home, depending on the region of the country where they were built. Double-sections ranged from 60-64%, and CrossMods, which are the most similar to a conventional home, ranged from 73-80%.

However, even this comparison does not fully account for quality differences between manufactured homes and conventional, site-built homes. The adjustments made in this study do not include any differences in code requirements, which include things like higher energy efficiency. Even the more stringent energy efficiency standards of the CrossMod are lower than conventionally-built home requirements, and the requirements for single and double-wides are lower still.5 The study also does not include any differences for things like wind speed design, which in many areas will be substantially higher for conventional construction. But these differences, while real, are likely relatively minor in terms of added cost, and are probably only responsible for a fraction of the cost differences between manufactured homes and conventional construction.

Another important factor when analyzing the cost of manufactured homes is how those costs have changed over time. We noted earlier that most manufactured goods typically rise in price less than overall inflation over time. However, this is not true with manufactured homes, which rise in price at roughly the level of overall inflation.6

So despite being factory-produced, manufactured homes do not see the same sort of cost trajectories as other manufactured goods — their cost trends look much more similar to those of conventional construction.

Overall, manufactured homes show that factory-based construction can, in certain cases, yield substantial savings over conventional construction. Even taking into account quality differences, a single-wide manufactured home is probably on the order of 40-50% cheaper than an equivalent site-built home. But manufactured homes also show us the limit to these sorts of factory savings. The greatest savings in manufactured homes are achieved with single-wide construction, when the entire house can be delivered as a complete, single unit, minimizing on-site work and eliminating any difficulties from breaking a home into multiple parts. But this is only feasible for homes small enough to be shipped in one piece. As you move away from this to larger and more complex homes, the cost savings of manufactured homes get eaten away: double section homes have less cost savings than single-wides, and with CrossMods — the homes that are most similar in features and performance to conventional construction — we approach the 15-20% “best-case” savings that we see with other forms of prefabricated homebuilding. And the fact that the cost trajectory of manufactured homes looks more like conventional construction than it does for other manufactured goods suggests that even with manufactured homes, factory-based construction hasn’t been an unlock for sustained productivity improvements.

Conclusion

The history of prefabricated construction is remarkably consistent. Cost savings of moving to factory-based production for homebuilding have consistently been much less (5-20%) than many practitioners hoped for, given the enormous reductions in cost that were achieved for things like car manufacturing. Often savings fail to materialize at all. Exceptions to this do exist, in the form of things like manufactured homes, but they serve as much as an illustration of the limits of factory-based homebuilding as they do its potential. Though many have tried to claim the mantle of “the Henry Ford of housing,” none have yet matched Ford’s achievements of transforming a craft-based industry into an industrialized one, driving down prices in the process.

This doesn’t mean that industrialization of homebuilding, or of construction more broadly, is a doomed enterprise. But they do show that it’s an enormously complex problem. The long history of prefabricated construction shows that we can’t merely move the construction process into a factory and expect enormous productivity improvements and massive reductions in cost. The problem must be tackled at a lower level of abstraction — understanding, what, specifically, is changing when a process moves from craft to factory production, how those changes result in productivity improvements and decreases in cost, and figuring out how we might apply those sorts of changes to the construction process.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housing

Thanks to California’s Prop 13, which limits annual property tax increases to 2% over the most recent sales prices, a very large (and increasing) fraction of homes in California are transferred through inheritance. “About 18% of all property transfers in the state last year, representing nearly 60,000 homes, were made through inheritance, according to a recent analysis by real-estate data firm Cotality. That share is a record for California in data going back to 1995, up from 12% in 2019. It is also roughly double the national share of 8.8% last year.” [WSJ]

Claims about the rising cost of housing’s impact on US fertility. Based on a (data-derived) economic model, so take with a grain of salt. “I vary housing costs directly within the model, finding that rising costs since 1990 are responsible for 11% fewer children, 51% of the total fertility rate decline between the 2000s and 2010s, and 7 percentage points fewer young families in the 2010s.” [X]

Los Angeles makes it easier to convert offices into apartment buildings. This is a perennially popular idea for increasing the supply of housing that is often hard to make work in practice (since these sorts of conversions are typically very expensive), but good to make it easier to do. [LA Times]

An interesting look at the largest homebuilders in the US, and the Japanese corporations that are acquiring them. I wonder if we’re finally starting to see the sorts of concentration in US homebuilding that we see in other industries. [Resiclub]

ONX Homes is a Florida-based prefab homebuilding company started by ex-Katerra folks, and using a Katerra-esque business model, that I’ve written about previously. There’s reports that things aren’t going well in their Austin division, and that a development they’re working on has been abandoned. [X]

Energy

Since historically it’s been prohibitively expensive to store electricity, the grid needs to balance electricity supply and demand moment to moment, bringing power sources online as demand rises and bringing them offline if it falls. If there’s a mismatch — such as if, say, dozens of data centers which collectively draw several thousand megawatts of power simultaneously disconnect because of grid disturbances — that can cause damage to the grid if its not carefully managed. “Early last year, a cluster of data centers in Virginia suddenly dropped off the power grid, threatening the stability of the already vulnerable system. The roughly 40 data centers, which had been using enough electricity to supply more than one million homes, simultaneously switched to backup power sources in February 2025, when a high-voltage power line malfunctioned. The sudden plunge in electricity demand forced the grid operator to take quick action to avoid potentially serious damage.” Grid operators have been able to handle these occurrences so far, but they’re apparently worried what might happen in the future if even larger disconnects occur. [WSJ]

China plans to invest $574 billion in upgrading its power grid over the next five years. [Reuters]

The Washington Post has an article on what might be an increasing willingness to embrace solar PV in the Trump administration. “A growing number of prominent Trump allies — including former House speaker Newt Gingrich, veteran strategist Kellyanne Conway and GOP pollster Tony Fabrizio — are promoting solar as electricity demand surges and energy affordability climbs the list of voter concerns.” [Washington Post]

Solar PV manufacturers are increasingly diversifying their businesses. [Bloomberg]

Cool graphic showing where solar panel efficiency records (the fraction of sunlight a panel can convert into usable electricity) have been set over time. It used to be the US developing increasingly efficient solar panels. Now those records are being set in China. [VoxDev]

The Department of Energy released the new nuclear safety rules that it had reportedly been updating. Apparently 750 pages, 2/3rds of the previous rules(!) have been cut, including what seems like references to ALARA (requirements that radiation exposure be “As Low As Reasonably Achievable.”) [NPR]

Read more

Many who look at the high and rising cost of housing see the problem as fundamentally one of production methods; more specifically, that homes could be built more cheaply if they were made using factories and industrialized processes, instead of assembling them on site using manual labor and hand-held tools. This idea goes back decades: in the 1930s, Bauhaus School founder Walter Gropius argued that the reason car prices had fallen while home prices hadn’t was because car manufacturing was highly automated, and home construction wasn’t. Nearly 100 years later, the construction startup Katerra raised billions of dollars in venture capital to pursue this same thesis, using factories and mass-production methods to deliver low-cost homes. (Full disclosure: I managed an engineering team at Katerra.)

One particularly ambitious effort to bring homebuilding into the world of mass production was Operation Breakthrough, a US government homebuilding program which ran from 1969 through 1974. A project of the newly-established Department of Housing and Urban Development (HUD), Operation Breakthrough was started to “break through” the barriers which prevented the large-scale adoption of industrialized building methods. It aimed to do this by attacking every part of the home construction process: funding new, industrialized methods of building homes, developing new codes and standards with which to evaluate them, and turning the highly fragmented housing market (characterized by numerous jurisdictions operating under different sets of requirements) into large pools of aggregated demand that could efficiently absorb large-volume home production.

While thousands of homes were built as a result of Operation Breakthrough, it ultimately failed in its goals to shift US homebuilding into a regime of industrialized building. Within a few years of the program concluding, most of the systems developed by Breakthrough were no longer in production, and prefabricated construction is a smaller share of US homebuilding today than it was in the 1960s before the program began. By looking at the history of Operation Breakthrough, and understanding what went wrong, we can better understand the barriers to industrialized homebuilding, and what overcoming them might require.

The Origins of Operation Breakthrough

In the 1960s, it was widely believed that the US was on the cusp of an enormous housing shortage. While homebuilding had been growing rapidly following the end of WWII (rising from 325k housing starts in 1945 to 1.9 million in 1950), the projected demand for housing in the wake of the baby boom was growing even faster. Birth rates rose from 2.2 children per woman at the depths of the Great Depression to 3.6 children per woman by the end of the 1950s. By 1960 the US had a population of just under 180 million, up from 140 million in 1945. The population was projected to reach 250 million by the mid-1980s, and over 300 million by the year 2000.

These millions were moving, more and more, to dense cities and metro areas. In a March 1965 address to Congress, President Lyndon Johnson stated that by the end of the century the US needed to build as many new homes as had been built since the arrival of the first colonists on American shores.

In the same Congressional address, Johnson called for the creation of a Department of Housing and Urban Development. The new cabinet-level agency would be formed from the existing Housing and Home Finance Agency (which in turn had been created in 1947 as an amalgamation of several other US housing programs). Within this new department would be an Institute of Urban Development, which would research technology that could reduce the cost of housing construction.

The bill creating HUD passed several months later, in August of 1965, but without the recommended research institute. However, the next year Congress authorized the creation of a “National Commission on Urban Problems” (later known as the Douglas Commission) which would study, among other things, various problems in the homebuilding industry. The following year, Johnson created a President’s Committee on Urban Housing. Johnson’s presidential commission was led by Edgar Kaiser, the son of famed industrialist Henry Kaiser, and the former general manager of Kaiser’s enormously productive wartime shipyards.

Both commissions studied ways to reduce housing construction costs, and considered whether prefabrication and/or mass production was a viable strategy for doing so. The Douglas Commission noted that while prefabrication of homes had resulted in some cost declines, no major “breakthrough” had occurred. With the proper encouragement, however, this might change:

The production of new products for the construction industry, experimentation with new materials and new production techniques, and exploration of advanced systems approaches to buildings, should be encouraged. Every effort must be made to eliminate roadblocks consistent with protecting health and safety. In the short run the greatest savings will be realized through increased scale and the use of existing prefabrication techniques at large scale. In the long run, wholly new systematized approaches may be forthcoming. [Emphasis mine.]

Kaiser’s presidential committee similarly noted that, while the housing industry was more efficient than was commonly believed, it was still “less dynamic and more resistant to change than most other major industries,” and that it “conspicuously requires stimulation through judicious public policies.” The committee wrote that:

The housing industry is operating with at least modest efficiency and has experienced more technological advances than the casual observer would suspect. The fiercely competitive structure of the industry encourages builders to adopt more efficient techniques as they are developed. On the other hand, the prevalence of institutional barriers, such as zoning ordinances and labor practices, and the low level of research in the industry, are signs that much progress can still be made.

As these reports were being prepared, Congress and the president were taking further steps to stimulate US housing production. The Housing and Urban Development Act of 1968, which allocated billions of dollars for housing development, was passed with the ambitious goal of creating 26 million new housing units over the next 10 years. At 2.6 million new homes each year, this was more housing than had ever been built in the US.

Most of the bill involved modifying or expanding existing government housing programs. However, one amendment of the bill (Section 108, later known as the Proxmire amendment), aimed to “encourage the use of new housing technologies in providing decent, safe, and sanitary housing for lower income families.” Per Section 108, HUD was required to create up to five plans for new housing technologies, build at least 1000 units of housing using each type of technology, and study the costs and benefits of each new housing type.

After the bill passed, HUD set to work implementing this program, seeking recommendations from the National Academies of Sciences’ Building Research Advisory Board (BRAB). BRAB recommended that HUD use the Section 108 technological program to test several homebuilding hypotheses:

• That major technological changes (as opposed to incremental changes) could dramatically improve productivity, reduce cost, and make it possible to produce more homes.

• That said technological change required large, aggregated housing markets, which could only be assembled by reducing onerous building codes, zoning requirements, and other regulations which had fragmented the housing market.

• That mass-produced homes would be found acceptable by the people living in them and the communities in which they were built.

Critically, the BRAB report strongly suggested that the program be an experimental one, to determine whether the above hypotheses were correct, rather than a demonstration program that assumed they were:

The program should be viewed throughout the planning, implementation, and subsequent evaluation phases as objective experimentation; the undertaking should not be allowed to be characterized as demonstrations of foregone conclusions nor to foreclose the evolution of other financial, organizational, or technological developments in the housing industry.

But even before BRAB’s report was complete, changes in the administration would shift how Section 108 was implemented. (This pitfall of ambitious government programs has been described as the “Law of Inescapable Discontinuity” — the fact that government programs are unlikely to be conceived and implemented by the same people.) Richard Nixon took office in January 1969, and he appointed George Romney, Governor of Michigan and the former CEO of American Motors, as Secretary of HUD.

Romney had competed with Nixon for the Republican presidential nomination a year earlier. Having won, Nixon may have nominated Romney for Secretary of HUD as something of a snub, and as a way of sidelining a political rival. Romney, however, considered the Secretary of HUD to be a “cabinet post of untapped potential where he could improve America’s cities and improve the cause of race relations.” During his tenure Romney conceived of many new HUD programs, even restructuring the agency to help transform what he saw as a collection of separate bureaucracies into something more organized and coherent.

Operation Breakthrough was one such Romney brainchild. The program was a larger and more ambitious undertaking than the Section 108 program which had been recommended by the BRAB report. Rather than a mere experiment that would build homes using new technologies, Breakthrough aimed to reorganize the entire country’s system of housing production. “What we are trying to do” said Romney:

is focus not only on technical ingenuity, but the whole concept of modern industrial management on each stage of the problem…The identification of markets; the identification and more effective use of available land; the design of the product and its environmental situation; and its financing and distribution to the consumer.

Romney’s background was in automobile manufacturing, and he strongly believed that mass production methods were the answer to America’s looming housing crisis; all that was needed was to clear the obstacles that had thus far prevented them from succeeding. Operation Breakthrough was thus directed “not only at technological advancement of housing,” but at “breaking through the various nonhardware constraints to more efficient production of housing.” To do whatever it took to industrialize homebuilding on a large scale.

Operation Breakthrough would be a three-phase program. In Phase I, HUD would solicit designs for industrialized housing systems — housing built in factories, or using like factory-like methods — and work to develop the most promising ones. In Phase II, the chosen systems would be constructed on several sites around the country to test their performance, see whether consumers would accept them, and to demonstrate the systems to prospective developers. In Phase III, large-scale production of the best performing systems would be undertaken. Concurrently with these phases, HUD would work to create the aggregate housing markets that could absorb large volumes of industrially-produced housing. This would involve working with state and local jurisdictions to relax code requirements, developing evaluation criteria so that developers could be confident houses built using novel technology would be “safe, sound, and durable,” and working with labor unions so that they’d accept the use of prefabrication. To administer this program, Romney appointed former NASA administrator Harold Finger. On the eve of the first human moon landing, and just months after Romney’s arrival at HUD, they began to build their housing moonshot project.

Phase I

In June of 1969, HUD sent a Request for Proposal (RFP) for industrialized housing systems to over 5000 organizations around the country. Respondents could submit either proposals in two types: Type A (well-specified systems for entire buildings) or Type B (systems that either had not yet been fully developed or were for only part of a building). Proposals could be for any type of housing system, from single family homes to high-rise apartment buildings. Responses were due in 90 days.

Despite the short window of time, HUD received 632 proposals, many more than they had anticipated. 244 proposals were Type A proposals, whole-building systems which were ostensibly fully developed. The proposals were for a broad array of different housing types — single family homes, townhouses, multifamily apartments — and were submitted by a variety of organizations. Some came from existing large-scale homebuilders, such as Levitt and Sons. Others came from existing prefabbers, like National Homes and Scholz Homes. Some were from manufacturing companies outside the homebuilding industry, including General Electric, Martin Marietta, and Westinghouse. Architects, universities, and building product manufacturers also submitted proposals. Some systems used volumetric modules (i.e., large boxes), others used panelized construction, sometimes in exotic arrangements: a system by architect Aitken Collins and Associates used foldable plastic sandwich panels to form a sort of three-dimensional A-frame, which could be erected in 2 to 6 hours “manually or with helicopter assistance.” Systems used both conventional building materials — wood, concrete, steel — as well as more exotic ones, such as plastic and carbon fiber.

Of the 244 Type A proposals, 22 were selected by a government panel to proceed to Phase I. Systems were chosen on the basis of whether they would be sufficiently practical and durable, whether they could cope with different site conditions, and whether the submitter had the necessary organizational and financial resources to actually produce the proposed system in volume. Selections were also made to ensure a breadth of different housing types, costs, materials, and degree of innovation (from the conventional to the radically new).

14 of the 22 systems were module or panel systems utilizing wood or concrete, and which were already in relatively widespread use. National Homes and Scholz homes, each selected for a Phase I contract, had already built tens of thousands of prefabricated homes in the US, and Rouse-Wates’ system had been used to build thousands of homes in Britain.

But some of the systems were more novel. Aluminum manufacturer Alcoa proposed a system which used aluminum-framed service modules (including kitchens, bathrooms, and other services like plumbing and HVAC) around which the rest of the house would be built. Pantek, a subsidiary of satellite manufacturer Ball Aerospace, proposed a panel-based system made from layers of epoxy, foam, aluminum, and plywood, which they had originally designed as a chemical-resistant flooring system for laboratories. Housing startup Stirling Homex proposed a concrete highrise system which would raise the building up on huge hydraulic jacks one level at a time, with individual modules slid in from below.

However, even these systems were often new implementations of existing ideas. Aluminum framing and craneless module erection via hydraulic jacks, for instance, were both old ideas by the late 1960s. Some commenters noted that there was little that was truly radical in the selected Breakthrough proposals, and program administrator Harold Finger admitted that “very little of what we are doing requires basic research or totally new hardware technology.” HUD defended its system choices on the grounds that the intent was to get systems into large-volume production as rapidly as possible, and they had selected systems based on their evaluation of whether the companies were able to do so; likewise, the short window (90 days) for response to the RFP gave scant time to develop a truly novel system from scratch. This naturally biased the evaluation towards systems that were less novel, and had less technical risk. As we’ll see, even the modestly innovative systems chosen often needed to be reworked to be more conventional.

One interesting outcome of the process is how many aerospace companies were included. GE (which specifically mentioned its aerospace expertise as relevant), Ball Aerospace (a satellite manufacturer) and TRW (developer of the ICBM) all were chosen for Phase I. Another selected participant, Material Systems Corporation, had been formed specifically to take aerospace innovations (such as composite materials) and apply them to the construction industry. And though it didn’t contribute a building system, Boeing was heavily involved in the overall project, managing first one, and then all of the project sites, as well as preparing various reports for HUD.

Breakthrough was conceived and implemented at the peak of the Apollo Program, and it was thought the approaches and organizations responsible for that success could be applied to other industries. The director of Breakthrough, Harold Finger, was a former NASA administrator and literal rocket scientist, and Breakthrough was deliberately modeled after successful aerospace and R&D projects. For instance, the Phase II project sites were managed using PERT/CPM scheduling methods that had been adopted from NASA and other aerospace development projects. Beyond the openness displayed towards aerospace companies, much of the language in the Breakthrough documentation is reminiscent of aerospace (electricity was installed in some buildings using “wiring harnesses”) and generally reflected the systems engineering approach NASA used to manage projects.

Following their selection for Phase I, the successful proposers set to work on their Phase II demonstrations. Many of the systems chosen had to be modified significantly before the Phase II contracts were signed. Christiana Western Structures, the modular housing subsidiary of the Christiana Oil Corporation, had originally proposed fully enclosed fiberglass-lined wall panels with a high-level of completion, such that they came from the factory with services like plumbing and wiring installed. Further development suggested that this highly integrated, prefabricated system would be too expensive, and it was changed to a more conventional open wall panel system. Aerospace manufacturer TRW had originally planned to use large, rotating mandrels to wrap box modules in a layer of fiberglass, but this was changed to use panels instead of volumetric modules. Overall, more than half of the 22 systems chosen had to be modified substantially prior to Phase II.

Part of the reason for these development difficulties was in how the systems were evaluated. The systems had originally been chosen by a panel of government evaluators, and the original RFP implied that they would need to meet the requirements of various existing building codes. However, HUD was convinced that inconsistent and varying building code requirements were a major impediment to large-scale adoption of industrialized construction. Thus HUD, instead of using existing codes, worked with the National Bureau of Standards to develop a set of guide criteria to evaluate the housing systems.1 These criteria were intended to make it easier to use factory methods and innovative technology by being performance-based: instead of specifying some material or building system (as was the case with many existing building codes), the guide criteria would specify some level of performance (i.e., requiring some level of strength, or durability, or fire resistance) giving designers the freedom to meet it in whichever way they deemed best.

However, in practice the guide criteria proved burdensome. The performance-based language was different and more complex from what many of the participants were used to, and many of the requirements (such as acoustic isolation) were substantially more stringent than existing code requirements. The guide criteria also demanded various performance tests of the systems be undertaken — such as impact, bending, and fire resistance — particularly for the more novel systems.

In part because of these difficulties, and in part because getting the project sites ready took longer than anticipated, the program was delayed significantly. It was originally planned for Phase I to be completed within four to six months, with construction on the prototype sites beginning in November of 1969. But by March of 1971 there were still no homes under construction. Despite these difficulties, sites were prepped and every system chosen was eventually developed to the point where it could proceed to Phase II — construction of the demonstration projects — and by September of 1971 all Phase II contracts had been signed.

Phase II

As the 22 chosen housing systems were being evaluated and developed during Phase I, HUD was working in parallel to find the sites where they would be built during Phase II. In the summer of 1969, RFPs were sent to jurisdictions around the country for sites where HUD could build demonstration homes with building code and zoning requirements waived or relaxed. HUD received 218 proposals, ultimately selecting nine sites in eight different states, each of which would receive several hundred demonstration homes from several different producers Altogether, just under 2800 demonstration housing units would be built.2

Prototype Site Planners — teams of architects, engineers, and other professionals — were contracted to design the layout of each site (building location, landscaping, etc.), and Prototype Site Developers were hired to manage the construction at each location, though these were later eliminated in favor of Boeing managing all project site construction. For each role, HUD selected several participants from a large pool of applicants

To try and speed up the program, work began on the prototype sites before Phase I was complete. By the end of 1970, ground had been broken on seven of the nine prototype sites, and construction would proceed over the next several years. This was a large, complex construction program — each site would have hundreds of housing units built on it, built using several different building systems (many of them novel), and marketed in different ways. Some units were sold as market-rate housing, others specifically for low-income residents (using HUD Section 236 financing), and others for the elderly.

Unsurprisingly, difficulties arose during construction. Though HUD worked hard to get local residents and officials on board with the program, they weren’t universally successful. Local residents were often not thrilled to have low income housing built near them, or were simply opposed to what they saw as the intrusion of “big government” into the private market. In Macon, the mayor reversed his support of Breakthrough and publicly renounced the project, though this wasn’t enough to stop construction; in Indiana, a local paper continually voiced its objections throughout the program; in Sacramento, a small group of vocal citizens hired a lawyer to try and overturn county approval.

Despite the efforts made by HUD to accommodate organized labor, there were several labor union-related disruptions to the project. A Teamsters strike in Sacramento delayed material deliveries, and Teamsters picketed the Breakthrough site for several days. In New Jersey, a union jurisdiction dispute about underground utility placement shut the job down for months, and there were further disputes regarding laying underground pipe and supervising the unloading of prefab modules. Likewise, though HUD worked to eliminate local code restrictions, there were nonetheless some complications. At some sites the local jurisdiction required changes to the building system designs before they would approve them, and in New Jersey the city building inspector, worried about the risk of relatively untested building systems, declared that “no codes would be waived” and that he intended “to apply the closest possible scrutiny to the project.”

Issues of transportation costs also arose. Most producers needed a factory within a few hundred miles of the jobsite for their system to be economically viable, otherwise transportation costs would exceed any factory savings from prefabrication. Two producers, Shelley and CAMCI, were originally slated to demonstrate their systems at the Memphis site, but they determined the market in that region was not strong enough to justify a nearby factory, and shipping from farther away would be uneconomic; both withdrew from that project site. Home Building Corporation was similarly slated to build using its system in Macon, but because of the 900-mile distance from its factory in Missouri, it determined that transportation costs would be too high, and so withdrew from that site as well.

There were also a variety of difficulties encountered with the building systems themselves. Many of these were the sorts of things that often come part and parcel with modular construction. Some modules were damaged during transportation and erection; some producers couldn’t arrange their modules to be delivered right when they were needed, so needed to temporarily store them on site. In some cases, “zip up” — stitching modules together, and finishing the interior — took far longer than anticipated. Leaks at joints in the modules, and in the flat roofs that some producers used, weren’t uncommon, sometimes due to poor quality control. The precast concrete system employed by Building Systems International initially had such poor quality that the developer halted erection work. (There, panel joints were of widely varying sizes, 20% of cast-in conduits didn’t align, and steel alignment was so poor that there were concerns of the systems’ structural integrity.)

Despite the evaluation that took place during Phase I, many of the more innovative systems proved difficult to implement in practice. This may’ve stemmed from the overlap between Phase I and Phase II, which caused the systems to be developed as site selection and planning were already underway . The developers worked to accommodate these practical difficulties.

Stirling Homex’s hydraulic jack system was abandoned in favor of conventional, crane-based erection, and Home Building Corporation similarly abandoned a plan to slide modules into place using a conveyor in lieu of a crane. But no system seems to have had more difficulties than the novel composite panels used by Material Systems Corporation. Fabricating the panels proved to be difficult and the design of the panels needed to be changed following extensive testing by the NBS. Once in place, the panels tended to leak, causing some residents of MSC homes to move out in frustration. The problems were severe enough that MSC’s demonstration project at St. Louis was cancelled, though MSC units were built at other sites.

Well-vetted systems used by established prefab companies, by contrast, appeared to encounter many fewer problems. By the time of Operation Breakthrough National Homes had built roughly 400,000 prefab homes over its history, and its Breakthrough operations appeared to go smoothly, with its deliveries better scheduled and its homes built faster and with higher quality than many other producers.

Because of the various difficulties and program delays, construction of the Phase II units took much longer than anticipated. Originally planned to be completed by November of 1970, it wasn’t until 1975 that all units were completed, sold, and occupied. But while some potential buyers didn’t care for some of the home designs (the “imaginative” design of the Hercoform houses proved slow to sell), occupants were quite happy with them overall. A 1974 survey of residents at eight different sites found that 90% of them were satisfied with their homes.

Phase III

Phase II production of the various building systems was relatively small-scale: a few hundred units for each system spread across the entire US, not enough to show any benefits from large-volume production. Phase III was when the systems would enter mass-production, and the benefits of scale would, hopefully, be realized. To incentivize the producers to enter large-scale production, they were promised Section 236 financing — a HUD program which subsidizes the construction of low-income housing — for up to 1,000 homes apiece.

Not every producer planned to enter Phase III. Some, like Stirling Homex and Townland, had gone bankrupt during Phase II. Others, like Pemtom, found that their systems weren’t economical and worth pursuing. All in all, 17 of the 22 building system producers planned to participate in Phase III.

Interestingly, most builders planned on licensing their systems, subcontracting the actual fabrication and construction to local contractors or fabricators. Few of the systems were innovative enough that they couldn’t be built by existing panelizers, precasters, or manufacturers.

But as these plans were being made, the political tide was turning against Breakthrough. As soon as Romney was appointed in 1969, he clashed with Nixon, and newspapers began speculating how long Romney would last in the administration. Throughout his tenure, Romney found the White House unwilling to sufficiently support his various program ideas, including Breakthrough: in 1969, Congress allocated $20 million to Breakthrough, which was $25 million less than had been requested and by the 1970s, further funding for the project appeared to be in danger. To counteract this, the program was accelerated, and Phase III production began before Phase II was completed.

These issues crested in August 1972, when Romney submitted his letter of resignation to HUD, citing “lack of access to the president and poor relations with White House staff members.” Nixon did not accept Romney’s resignation, convincing him to remain until after the 1972 election. In January of 1973 both Romney and Harold Finger were replaced by new appointees who did not have the same enthusiasm for Breakthrough, and that same month Nixon cut funding for any additional Section 236 projects.

The turn against Operation Breakthrough was not merely because Romney and anyone associated with him had become “persona non grata” in the Nixon administration, though that appears to be part of it. Breakthrough had been organized for the express purposes of meeting huge projected demand for housing, because it was believed that the existing homebuilding industry would be unable to step up to the challenge. But this proved to be incorrect: existing, conventional homebuilders essentially doubled output from 1.2 million units in 1965 to nearly 2.4 million units in 1972. On top of this, a method of factory-built housing outside of Breakthrough — mobile homes — was proving more and more popular, rising to nearly 600,000 units being produced annually by 1972. In light of these developments, Operation Breakthrough appeared much less necessary than it did at the start of the Nixon Administration.

Additional Section 236 funding was cut before Phase II of breakthrough was complete: construction was still in progress on most of the prototype sites. But thanks to the acceleration of Phase III, the already-allocated Section 236 funding remained available, and by 1975 around 25,000 units of Phase III housing were under construction. In addition, another 7,000 units were being built outside of Section 236 funding.

But these tens of thousands of units were not enough. Without a sustained source of government funding, Phase III proved to be a brief blip, rather than the seed of a new, industrialized homebuilding regime. The housing system producers continued to withdraw their systems from the market. From the 17 that planned to participate in Phase III, only 14 actually did so. By 1976, only 5 systems were still being marketed. Hercules Chemical company had invested $10 million in production facilities for its “Hercoform” housing system, but it sold off its factory in 1973 after three years of losses in the business. Levitt and Sons had similarly spent $3 million on a highly automated factory to produce its building system. The factory came online in 1971 and was shuttered just three years later. Alcoa apparently sold around 50,000 of its “heart” units, but ceased producing it in 1977. GE similarly left the housing business in the late 1970s. Scholz Homes, founded in 1946, closed its factory and ceased operations in 1983. Only National Homes and FCE Dillon appeared to still be in the homebuilding business by the mid-1980s; neither company is in business today.

Altogether, Operation Breakthrough cost the government around $72 million (around $500 million in 2026 dollars): $22.1 million spent on Phase I development costs, and $49.5 million on compensating builders for the difference between housing system costs and their market value.

Why did Operation Breakthrough fail?

Operation Breakthrough is generally considered to be a failure. The original goal — to rapidly introduce mass-produced, industrialized construction methods to the homebuilding industry — wasn’t achieved. A 1976 GAO report on the outcomes of Operation Breakthrough noted that it “did not create the large, continuous markets necessary for efficient industrialized housing construction or document and obtain answers to questions on cost savings to be gained by using such construction methods.” In 1971 George Romney predicted that by the 1980s at least two-thirds of new homes would be factory-built. The actual fraction in 1984 was 36%, roughly half of his prediction, with factory-built homes constituting a falling share of new housing ever since. Today it’s roughly 10%, most of which are manufactured (mobile) homes. Outside of manufactured HUD-code homes, factory-built homes make up about 3% of the US single family home market, and an even smaller share of the multifamily apartment market.

The proximate reason that Breakthrough failed seems to be overextension. The program tried to do too much, too fast: the original BRAB report cautioned that investigating large-scale production of novel housing technology should be pursued as an experimental program, designed to answer questions and gather information, rather than a demonstration program that assumed such methods would be successful. But while Breakthrough kept some of the trappings of an experimental program, it became a rushed demonstration program in practice. Respondents to the RFP were only given three months to submit a building system, little time to develop much in the way of truly novel systems, and the criteria respondents were judged by favored the selection of existing technology. The guide criteria used to evaluate them weren’t ready when the RFP was sent out and the systems were chosen, and several building systems had to be redesigned once they became available. Construction on the prototype sites similarly began before analysis of the existing systems, resulting in numerous difficulties with the more innovative methods. Funding was cut for the program before construction was even finished on the prototype sites, and little was done in the way of market aggregation or of technology development — there were no broader union agreements, no further work to develop or secure wider adoption of the guide criteria, no research funding for iterating on the system designs. (One report speculated that Breakthrough was able to get the original union agreements for the program only because the unions believed Breakthrough would be a short-term program that wouldn’t have larger impacts.)

To have a chance at succeeding, Breakthrough likely needed more government support, over a much longer period of time. A 1970 article in Progressive Architecture argued that the program needed “more money, more staff, and the guarantee of a large market to ensure that its goals can be reached.” A 1975 study of the program by the Real Estate Research Corporation concluded that “one principle conclusion of the program is not to attempt too much too quickly because the adoption and diffusion of an innovation is not an instantaneous process. A 1974 report by the National Academies of Sciences noted that “government housing programs must be planned on the basis of a long view” and that “the timeframe allocated for reaching Operation Breakthrough objectives proved unrealistic.” And a 1976 analysis of government demonstration programs by the RAND Corporation succinctly noted that “Breakthrough had too many program objectives and too little time and money to achieve them.”

I would argue that the deeper reason behind the failure of Operation Breakthrough is the elusive nature of the benefits of industrialized building methods. It has long been believed that factory-based construction will yield the same benefits for housing as it has for manufacturing: dramatic improvements in production efficiency, and dramatic reduction in prices. But in practice these benefits have been difficult to achieve. Sweden, which has large-scale adoption of factory-built methods, does not appear to benefit from substantially improved productivity or decreased homebuilding costs. National Homes, one of the participants in Operation Breakthrough built half a million prefabricated homes in the US over the course of its history, but its prefabricated methods didn’t transform the industry the way Ford’s assembly line transformed car manufacturing. More than 30,000 housing units using Breakthrough systems were ultimately constructed, but they nevertheless had difficulty competing with conventional, site-built construction, and none of the systems survived outside of government support.

This doesn’t mean that there are no benefits to be had from prefabrication: we can see the cost benefits in things like manufactured (mobile) homes, or of precast concrete parking garages, which are far less expensive than alternative construction methods. And it doesn’t mean that something like Breakthrough couldn’t work. But to be workable, such a program would need a much greater level of government support than Breakthrough got, and it would need to be rooted in understanding of the actual mechanisms that make it so difficult to drive down costs with factory-based homebuilding.

Welcome to the reading list, a weekly roundup of what happened in infrastructure, buildings, and building things. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

No newsletter this week, but I’m working on a longer essay about the history of Operation Breakthrough (a greatly expanded and more thorough version of an older essay) that will be out next week.

Housing

Its obvious that getting housing projects permitted in the US is often quite difficult, but it’s not always obvious how difficult. Previous research by economist Ed Glaeser has tried to quantify this by estimating the “hedonic” value of land (how much people would pay for a given amount of land space), which gives an implied value for how much the “permit” portion is worth. Now Economists Evan Soltas and Jonathan Gruber have a paper out looking at how much of a burden permitting is in the city of Los Angeles in dollar terms. From the abstract: “Permitting costs are widely cited, but little analyzed, as a key burden on housing development in leading U.S. cities. We measure them using an implicit market for “ready-to-issue” permits in Los Angeles, where landowners can prepay permitting costs and sell preapproved land to developers at a premium. Using a repeat-listing difference-in-differences estimator, we find developers pay 50 percent more ($48 per square foot) for preapproved land. Comparing similar proposed developments, preapproval raises the probability of completing construction within four years of site acquisition by 10 percentage points (30 percent). Permitting can explain one third of the gap in Los Angeles between home prices and construction costs.” Would love to see more research like this for other metro areas [X]

Restricting institutional investors from owning single family homes continues to be a major political talking point, but I remain unconvinced that this has much impact on home prices. More evidence for this: A 2022 ban on investors from buying homes to rent in the Netherlands didn’t affect home prices [SSRN]. And there doesn’t seem to be much relationship between institutional ownership and home price appreciation at the city level. [Progressive Policy]

The Atlantic has a good article about how high-end housing can increase housing supply across income levels. When people move into a new, expensive unit, many of them will move from lower-cost units, which in turn will be occupied by people moving from even lower cost units, and so on. “...three researchers looked in extraordinary detail at the effects of a new 43-story condo project in Honolulu. The building, called the Central, sits right behind the giant Ala Moana shopping center, halfway between downtown and the beachfront hotels of Waikiki. It comprises both subsidized and market-rate units, priced at around $780,000 for the former, and $1.25 million for the latter. What the researchers found was that the new housing freed up older, cheaper apartments, which, in turn, became occupied by people leaving behind still-cheaper homes elsewhere in the city, and so on. A new rung higher up the housing ladder permitted people lower down to climb. The paper estimates the tower’s 512 units created at least 557 vacancies across the city—with some units opening up no empty apartments (if, say, an adult child moved to the Central from their parents’ home) and others creating as many as four vacancies around town.” [The Atlantic]

When population peaked in various US counties. [X]

How home prices have changed in several countries over the last several years. Why are prices up so much in Mexico? [X]

IFP colleague Connor O’Brien noted that statistics about the skyrocketing age of homebuyers in the US is based on a mailed survey by the National Association of Realtors that is far higher than other estimates. [X]

Manufacturing

It really seems to be the end of an era for Japanese TV manufacturing. A few weeks ago Sony spun off its TV business into a joint venture with China. Now Panasonic is exiting the TV business as well. “Today, it announced that Chinese company Skyworth will take over manufacturing, marketing, and selling Panasonic-branded TVs.” [Arstechnica]

From the annals of “environmental laws give NIMBYs the tools to endlessly delay projects.” Construction of a $100 billion Micron memory fab in New York is being held up by a lawsuit from six local residents who oppose the project. They’re arguing that the environmental impact study (which took nearly two years to complete) was “unnecessarily rushed.” [X]

Expanding US electricity generation capacity has been bottlenecked by gas turbine suppliers, but it looks like the major manufacturers are significantly expanding their capacity. ““We expect at least 19 GW of total available equipment capacity by 2028, increasing to 49 [GW] and 76 GW by 2029 and 2030,” Jefferies said.” [Utility Dive]

The Economist on the Chinese threat to German manufacturing. “What many Germans call the “China shock 2.0” plays into fears that the country’s industrial heart is being hollowed out. In Baden-Württemberg, a rich state that holds an election on March 8th, candidates are issuing dire prophecies about becoming the “Detroit of Europe”.” [The Economist]

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Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housing

A map of how home prices have shifted in the last year. Prices in the midwest and parts of the northeast are up, prices in the south are down. [X]

Interestingly, this map lines up really well with this map of what time period the largest fraction of homes in a given area were built in. In the midwest and northeast, for most areas the largest fraction were built prior to 1939. In the south, for most areas it’s post-2000. [X]

Also, the places with the biggest COVID housing price booms are now the places where it’s the hardest to sell your house. [X]

Bloomberg has a piece looking at different decisions made by homeowners, renters who expect to buy a home someday, and renters who expect never to own a home. One interesting datapoint: for low-income brackets, renters have a higher propensity to invest in crypto, and a higher rate of reporting that they put in low effort at work. [Bloomberg]

Related, in an essay about California’s pivot to anti-growth in the 1960s, I noted how Prop 13, which cut property taxes and locked in extremely low tax rates, cemented the opposition to growth that had been rising since the 1960s, and that there were similar “tax revolts” in states across the country. Now it seems like opposition to property tax is gaining traction again: Florida’s house of representatives just passed a bill that would remove non-school property taxes for all “homesteaded properties” (which, as I understand it, basically means primary residences). [Florida Phoenix]

Manufacturing

The Rhodium Group has a good article breaking down why Chinese EVs are so cheap. It doesn’t seem to be about labor productivity (which is lower than Western firms), or subsidies. Instead, the advantage mostly comes from the level of vertical integration they’re operating at. “For BYD, vertical integration is the single most important factor behind the company’s price advantage. That said, even among Chinese OEMs, BYD and Leapmotor are outliers. This helps explain why BYD has been among the companies most consistently leading price decreases over 2024 and 2025. Vertical integration requires higher upfront capex and R&D, but it eliminates supplier markups across a much larger share of the vehicle.” [RHG]

Discussions between Ford and the Trump administration about setting up Chinese car plants in the US via joint ventures. This idea keeps coming up. [Bloomberg]

Thanks to the trade war with China, and the huge amount of chips and other electronics the US is importing from Taiwan for data center construction, the US now imports more from Taiwan than it does from China. [X]

Similarly, thanks to the huge demand for memory chips from AI data centers, computer memory manufacturer Micron is spending billions of dollars on new memory fabs in the US. “In Boise, where the company is based, Micron is spending $50 billion to more than double the size of its 450-acre campus, including the construction of two new chip factories, or fabs…That’s not all. Near Syracuse, Micron just broke ground on a $100 billion fab complex that represents the state of New York’s largest-ever private investment.” [Wall Street Journal]

Likewise, TSMC is planning to spend $100B on four more Arizona fabs. [Tech Powerup]

The WSJ on the EV writedowns of the US auto manufacturers. “More than $20 billion in previously announced investments in EV and battery facilities were wiped out last year, according to Atlas Public Policy, which tracks clean-economy investments. That drove the first net annual decrease in announced investments in years.” [Wall Street Journal] Related, car manufacturer Stellantis sells its stake in a Canadian battery manufacturing plant for $100. [Detroit News]

US vaccine manufacturers are struggling: thanks to RFK’s anti-vaccine efforts at the Department of Health and Human Services, vaccine sales are down, jobs are getting cut, and investments in new vaccines are being scaled back. And it seems like things might get even worse for them. “A major concern for the big companies is whether the Trump administration will do away with the special liability protections afforded to vaccine makers that have helped them stay in the market.” [NYT]

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If you’re only going to run your process once, or just a handful of times, these opportunities are considerably narrowed. It’s obviously hard to justify the time and effort it takes to design a really efficient production process or invent some new manufacturing equipment if that process is constantly changing.

An example of this playing out in practice is the different cost trends of cars vs. car repair. In inflation-adjusted terms, cars have steadily gotten cheaper over time. The cost of car repair, on the other hand, has steadily gotten more expensive, rising mostly at the rate of overall wages (and recently, even faster).

Much of this difference comes down to the nature of the processes at work. Cars are manufactured via a repetitive, high-volume process that spits out nearly identical models by the hundreds of thousands or millions. Car manufacturers can justify spending billions of dollars designing a new model of car and the process for making it, because that cost will get spread out over a huge number of cars. Repairing a damaged car, on the other hand, is different: for a given model, any given repair process will be run a much smaller number of times, or maybe only once (since cars might get damaged in accidents in unique ways). A repair facility will need to accommodate a huge number of different models and model years, each damaged in different ways. There’s much less opportunity to design an efficient, highly automated repair process.

There are some complications to this basic pattern — the Toyota Production System and its descendents were designed to get mass-production-style benefits for a much more variable production process by making that process more flexible — but they don’t change the fundamental logic.

Thus, for things that we can repetitively produce in very large volumes — cars, transistors, LCD screens, corn — we’ve gotten good at making them very cheaply. Things produced in much smaller volumes, or where we need to adapt our process on the fly based on the specific situation, are much harder to produce cheaply. One way of thinking about services, which tend to get more expensive in inflation-adjusted terms over time, is that they’re things which generally require a lot of situation-specific adaptation, and can’t be produced via some high-volume, highly repetitive process.

An important aspect of this is automation. I’m fond of pointing out that it’s generally possible to build a machine to perform any particular task (and it has been for quite some time). If you’re going to do some task thousands or millions of times, it’s long been possible to automate that task with some sort of dedicated machine. (People skeptical of humanoid robots are very fond of pointing out how this sort of hard automation is far more efficient than a human-shaped robot at doing some task.) The challenge with automation has historically been flexibility: creating a machine that can make adjustments on the fly, perhaps changing the sequence of tasks completely as the situation changes, the way a human can. Even if the hardware itself can be used to perform a variety of different tasks, information processing capabilities have been limited; it has taken a lot of time and effort to get any particular automated process working, which could only be justified if those costs could be amortized over a sufficiently large volume. This is why the car industry has by far been the biggest user of industrial robots historically, as they have the right combination of very high production volumes, and frequent (but not too frequent) process changes (since models change yearly).

But this is changing: automation technology is getting more and more flexible. Computer vision has advanced, billions of dollars are being poured into developing humanoid robots, and a panoply of AI technologies are making it possible for an automated system to flexibly respond in a highly variable environment. Self-driving cars are one example. Being able to drive between any given two points, responding to situations or disruptions as they appear — traffic lights, pedestrians, other cars — is the exact sort of thing that automation historically has been very bad at, but that technology is now chipping away at.

As automation technology gets better and better, I have been thinking about how it will get pushed into areas requiring low-volume production or situation-specific adaptation that previously have been resistant to it. One potential trajectory is that with better, more flexible automation, “minimum efficient scale” — the size of an operation you need to be competitive — shrinks. With sufficiently capable robots, for instance, it might become possible to efficiently produce things in really small-footprint, low-overhead factories. The idea of “microfactories” is something people are enthusiastic about: you often see it in various prefab construction startups, but that excitement has spread elsewhere. The premise of the (now-defunct) EV startup Arrival was building cars using these sorts of highly flexible microfactories.

But another possible trajectory is in the opposite direction: large-scale, highly efficient production operations which capture significant economies of scale, but which produce a very wide range of outputs. Factories producing millions of different products in low volumes, or even quantities of one. I’m tentatively calling this idea “AWS for everything.”

AWS and flexible automation

AWS (Amazon Web Services) is Amazon’s cloud computing business. The idea of it (and of other similar offerings like Microsoft Azure and Google Cloud Platform) is that instead of needing to set up your own computing infrastructure to do things like host a website or store large amounts of data, you can just rent it from Amazon. Amazon builds the data centers, sets up the servers, and creates the software tools and infrastructure that other people can use to set up and manage their computing needs.

Making this work as a business demands a huge amount of expensive infrastructure; even before AI, Amazon and other cloud computing companies spent a huge amount of money building data centers in various regions. But as Ben Thompson notes, AWS “benefits tremendously from economies of scale.” The more customers AWS has, the more efficiently it can use its infrastructure, similar to how electric utilities wanted lots of customers to reduce demand variability and achieve higher utilization rates. Thus with AWS you get a highly variable output — millions of different websites and computing tasks — supported by large-scale infrastructure investments. You can very quickly use Amazon’s infrastructure to perform whatever computing task you’re interested in, from hosting a small website to processing terabytes of data, without needing to build or operate any of that infrastructure yourself.

This same basic logic applies to physical automation. If you have machinery or equipment that can perform different sorts of tasks or produce a variety of different goods, and an effective software control layer that can tell each piece of equipment what it should be doing and where material should be routed, you can automatically produce a very large variety of different things. And the larger your operations, the lower your marginal costs of production: the more you produce, the greater your equipment utilization rate, and the more you can capture other economies of scale, such as using more efficient high-volume equipment.

Historically setting up this sort of highly automated, highly flexible production operation has been limited by the fact that setting up any particular automated process took a great deal of time and effort, and the technology didn’t exist for that automation to respond flexibly to a highly variable environment. So automated production lines, even ones that used flexible technology like robotics, could only be justified for high-volume production, and the range of variation they could accommodate was fairly limited.

But as automation and AI get better, this becomes much less true. If your software is smart enough, and your equipment flexible enough, you can set up some new process to produce some new widget on the fly, automatically working out what the process steps need to be and how to route the material through the various machines, without needing to take the time and effort to dial it in that was required historically. And if your volumes are high enough — if you’re producing enough different widgets, each with its route through a sequence of machines, sharing processing steps where possible — your costs for each individual unit of production might be very low indeed, even as you produce a wide variety of different things. So I can imagine having very large-volume production operations, which obtain large economies of scale and produce a wide variety of different outputs. Huge warehouses filled with all sorts of different machines, materials, parts, and components being routed between them, paths and tasks changing on the fly, a panoply of different goods rolling off the equivalent of the assembly line, each one sent to its final destination by low-cost, small-scale delivery vehicles like drones or Austin Vernon’s pallet EVs. Customers could spin up production on this rented equipment and start producing whatever they wanted without having to build their own factory. These sorts of operations wouldn’t displace traditional mass-production style processes (which will still have a substantial cost advantage), but would exist alongside them.

(You probably don’t even need to completely automate the hardware side, so long as you have a sufficiently intelligent control layer. Uber’s mapping software can direct a driver to where they need to go, leaving the driver to actually turn the wheel and work the controls. Amazon has similar software that tells its distribution center workers where to pick up and bring packages. So you can imagine humans acting as much of the “connective tissue” in this sort of production process, being directed by software telling them where to go and what to do to maximize utilization.)

AWS for everything

You can see the seeds of this “AWS for everything” concept in some businesses that exist today. In manufacturing there are fabricators that specialize in high-mix production like SendCutSend, OSH Cut, or JLCPCB. You send your part design to SendCutSend: their software automatically checks to see if it can be fabricated using their equipment (laser cutters, CNC machines, etc.), and they send you back the part a few days later. According to SendCutSend’s founder Jim Belosic, this model only works because of economies of scale, being able to efficiently spread the costs of their millions of dollars of equipment. As he said on Tool or Die:

The key with high mix is that it actually works at scale. The larger volume of high mix, the easier things get...Especially with sheet cutting. With sheet cutting, the software side of us, it allows us to take hundreds of different customers, with a quantity of one part each, and put them onto a sheet, like tetris, nested all together, and run it all at once. So we only do one setup, for potentially dozens or hundreds of customers, we do one load into the machine, we only retrieve the material once. And we have really good sheet utilization, we have almost no scrap. It’s probably one of the lowest in the industry.

It doesn’t work though, when you only do a few. If I was to run one of those customers at a time, we’d be bankrupt.

SendCutSend has grown rapidly — founded in 2018, they recently passed $100 million in annual revenue — but they still work hard to maintain flexibility, using equipment that doesn’t require months of downtime to reprogram or configure when processes change. They’re also expanding their offerings. They started with laser cutting, later added CNC machining, and now offer welding of single parts. They’ve also gradually expanded the range of materials that they offer. You can imagine that as automation gets better and better, this sort of business model could continue to be extended, going to multi-part welding, assembly, and eventually entirely finished goods.

And it’s not just manufacturing where this sort of production model might emerge. I was inspired to write this essay after reading a really great essay about lab automation at Owl Posting, speculating that various lab automation startups might converge on being “AWS for biotech”: large, automated labs that can spread the costs of their automation over a large number of experiments run for different customers. Right now much of this sort of lab work isn’t automated, not because it’s not possible to automate but because it’s not repeated enough to be worth it in any particular lab. Centralize all those experiments in one place, and maybe that changes:

If you are to accept that lab centralization (as in, cloud labs) means you can most efficiently use lab robotics—which feels like a pretty uncontroversial argument—it also means that the further you lean into this, the more able you are to vertically integrate upstream. If you’re running enough experiments such that your robots are constantly humming, you can justify producing your own reagents. If you’re producing your own reagents, your per-experiment costs drop. If your per-experiment costs drop, you can offer lower prices. If you offer lower prices, you attract more demand. If you attract more demand, your robots stay even busier. If your robots stay even busier, you can justify producing even more of your own inputs. And so on, ad infinitum, until you devour the entirety of the market, and the game of biology becomes extraordinarily cheap and easy for everyone to play in.

I’m not a scientist, but I can imagine how this sort of model could apply to other areas of scientific research as well — chemistry, materials research, etc.

How far could this model be pushed? I opened this essay talking about car repair, which has risen in cost far faster than the actual production of cars. I’ve been in car accidents where the damage was relatively minor, but that nevertheless cost a large fraction of the entire value of the car to repair, due to the un-optimized, un-automated, labor-intensive repair process. Could we have some sort of large, centralized car repair facility, spreading the cost of its automated equipment (heavy industrial robot arms, lifts, welding robots, perhaps even metal fabrication equipment) across a huge number of repaired cars?

It’s not obvious to me whether this would work for car repair. Whether “AWS for everything” will work in a given industry will depend on the specifics of that industry, the costs and capabilities of the equipment available, and what scaling effects look like. If equipment is relatively inexpensive, and there aren’t substantial economies of scale at work, I wouldn’t expect this sort of production arrangement to necessarily make sense. A few years ago people were very enthusiastic about this sort of model for cooking, with “ghost kitchens:” commercial kitchens without any sort of dine-in option, preparing food for delivery-only restaurants. Some of the supposed advantage of ghost kitchens was that they required much smaller amounts of space that could be located outside of expensive, high-traffic areas (since you didn’t need any sort of dine-in option). But ghost kitchens were also expected to have economies of scale. Multiple different “restaurants” could be served from the same facility, possibly taking advantage of batch ingredient prep or high-capacity equipment. But while ghost kitchens are still around, they don’t seem to have been the enormous success they were originally predicted to be. (Possibly this will change if food prep automation gets much better, but that’d be somewhat surprising to me.)

So for many industries the “AWS for everything” model won’t work. But I nevertheless think there’s a good chance that certain kinds of production — manufacturing, certain sorts of scientific research, other capital-intensive services — will be organized this way in the future.

Thanks to Austin Vernon for reading a draft of this. All errors are my own.

Welcome to the reading list, a weekly list of news and links related to buildings, infrastructure, and industrial technology. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.

Housekeeping items this week:

• My book is a finalist for the Manhattan Institute’s Hayek Book Prize.

Housing

The Atlantic has a piece on how difficult and user-unfriendly most smart home technology still is. This was true when Gizmodo published its 2015 article Why Is My Smart Home So Fucking Dumb?, and it seems like it’s still true today. [The Atlantic]

The Department of Justice is apparently considering opening an antitrust probe into US homebuilders, possibly due to their coordination on prices through a trade group. “Leading Builders of America”. [Bloomberg]

The US house of representatives passes the Housing in the 21st Century Act. This is the house version of the ROAD to Housing Act which was passed by the senate back in October, and which I talked about on Statecraft. Among other things these bills remove the requirement that manufactured (mobile) homes have steel chassis, which the industry has long complained about. [X]

Trump and newly elected New York mayor Zohran Mamdani are apparently both enthusiastic about NYC zoning reform. [Politico]

Americans are taking on more and more credit card debt, but mortgage delinquencies so far remain fairly flat. [X]

Buildings are apparently more frequently collapsing in the Southern Mediterranean, possibly due to increased erosion due to rising sea levels. “Alexandria, a historic and densely populated port city in Egypt representative of several coastal towns in the Southern Mediterranean, has experienced over 280 building collapses along its shorelines over the past two decades, and the root causes are still under investigation.” [Wiley] [Usc]

Sunderji’s Paradox: the rich spend a smaller fraction of their income on their housing than the poor, but as countries get richer these fractions don’t change. [Substack]

“London has been set a target of building 88,000 new homes per year over the next decade. Last year construction started on just 5,891 — 94 per cent below target, a 75 per cent year-on-year decline, the steepest drop in the country, the lowest tally since records began almost 40 years ago and the lowest figure for any major city in the developed world this century.” [FT]

Manufacturing

The WSJ has a piece on Corning, the glass company that’s manufacturing the suddenly-in-demand fiber optic cable for AI data centers. “In 2018, Weeks and O’Day went to Dallas to tour a data center owned by Meta, then known as Facebook. They marveled at the demand for fiber-optic cabling to connect all the servers inside that giant warehouse. Facebook was using a mix of copper cables and existing fiber optics, but found both ill-suited to the task. This spurred Corning’s engineers to make their cables thinner, but also tougher, so they could withstand tight bends, says Claudio Mazzali, Corning’s head of research. Five years later, ChatGPT made its debut, and demand for fiber-powered data centers exploded. “We’re thankful that we made the trip in 2018 and thankful that we made the bet,” says O’Day. At the time, they had no idea whether it would be a good investment or a dud, he adds.” [Wall Street Journal]

In other glass manufacturing news, the WSJ also has a piece about windshield manufacturers upset about a US-based, Chinese-owned windshield factory making windshields for far cheaper than they can. [Wall Street Journal]

Bloomberg has a piece on whether its only a matter of time before Chinese cars are available in the US. One interesting point is that it’s actually Korean and Japanese imports (which dominate the low end of the US market), not US brands, which might be most threatened by an influx of low-priced Chinese cars. [Bloomberg]

BYD’s January sales were 30% lower than last year. [X]

A US drone manufacturer was booted out of their space at the Brooklyn Navy Yard, apparently in part due to activist pressure upset that they were supplying drones to Israel. [Mondoweiss] [X]

The Whitehouse released a maritime action plan for revitalizing US shipbuilding. I haven’t had a chance to read through it closely, but it seems to be a collection of a few dozen policy recommendations. [White House]

We’ve previously noted that a big drawback of tariffs is that they can make domestic manufacturing less competitive by jacking up the price for inputs to manufacturing. Now the Trump Administration plans to relax the tariffs of metal and aluminum. [FT]

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For decades, American construction has fallen behind almost every other major sector in productivity growth. As far back as 1970 researchers noted that construction productivity improvement significantly lagged productivity improvement in the economy overall, and by 1985 economists were investigating what appeared to be declining construction productivity. Stanford civil engineering professor Paul Teicholz noted in a 2004 article in AECbytes that between 1964 and 2004, construction productivity declined by 0.59% per year on average, which was “particularly alarming when compared to the increasing labor productivity in all non-farm industries, which have experienced an increasing productivity of 1.77%/year over the same time period.” A 2017 article in The Economist noted that “construction holds the dubious honour of having the lowest productivity gains of any industry.” In a 2023 New York Times column, Ezra Klein wrote that “A construction worker in 2020 produced less than a construction worker in 1970, at least according to the official statistics.”

The trend of construction productivity in the United States failing to improve over time is indeed concerning. “Productivity” means some measure of output, divided by some measure of input. When productivity is improving, we get more output for a given amount of input over time; if productivity is falling, we get less output for a given amount of input over time. If productivity doesn’t improve, we can’t expect construction costs to fall and things like houses, roads, and bridges to get any cheaper. Because of this, it’s worth looking deeply at what exactly the trends in US construction productivity are.

Economists and researchers measure construction productivity in a variety of different ways. We can broadly categorize these metrics by their level of granularity:

• At the lowest level of granularity, we have metrics that track productivity changes across the entire construction sector.

• Slightly more granular are metrics that look at productivity changes in a particular subsector, such as housing construction.

• Looking more specifically, we have metrics that look at productivity changes for constructing particular buildings.

• And finally we have metrics that track productivity changes for individual construction tasks.

Each category of metric gives a slightly different perspective on productivity trends, and each has its own measurement challenges that we must consider when interpreting the data.

Sector-wide productivity metrics

Sector-wide productivity metrics look at productivity trends across the entire construction industry. They answer if, overall, we’re getting more or less construction output for a given amount of input. The graph below, for instance, shows trends in US construction productivity by using total construction spending as a measure of output, and total hours worked in the construction sector as a measure of input. (Spending has been adjusted to 2025 dollars using the Consumer Price Index —we’ll talk more about whether this is a reasonable way to adjust for inflation later.)

We can see that, per this metric, construction labor productivity — the amount of construction output we get for a given amount of labor — is virtually flat between 1964 and 2024, whereas labor productivity in the economy overall rose by a factor of three.

Sector-wide metrics which look at productivity trends across the entire construction industry are very common. Paul Teicholz uses the same data we used above to look at trends in construction productivity in a 2013 article, and his 2004 article uses a very similar metric (rather than total spending, he uses US Department of Commerce construction spending data, a subset, as a measure of output).

In their 2025 paper “The Strange and Awful Path of Construction Productivity in the US”, economists Austan Goolsbee and Chad Syverson use a slightly different sector-wide productivity metric. For output they use real (inflation-adjusted) construction value-add data from the Bureau of Economic Analysis, and for input they use the number of full-time construction employees. (Unlike total construction spending, which just tracks the value of the outputs, value-add measures the value of construction outputs minus the value of the inputs used.) Goolsbee and Syverson also look at trends in construction total factor productivity (TFP), which measures productivity of both labor and capital (equipment, machinery, etc.) by comparing the growth rates of real construction value-add to the growth rates of construction labor and capital inputs. According to Goolsbee and Syverson’s productivity metrics, construction productivity looks even worse. Productivity increased from the 1950s until the mid-1960s, but since then it has declined by roughly 50%.

Discussions of US construction productivity often reference this Goolsbee and Syverson paper, or the data behind it. An early version of Goolsbee and Syverson’s paper is what Ezra Klein is referring to in his 2023 New York Times column, and it’s referred to in a 2025 Federal Reserve Economic Brief examining productivity. The data is also used in a 2026 report from Goldman Sachs looking at the causes of low US construction productivity. Management consultancy McKinsey likewise uses BEA value-add data in a 2017 report to construct a similar productivity metric, gross value add per hour worked, to show that in the US construction productivity improvement had lagged virtually every other industry:

The Bureau of Labor Statistics also uses BEA data, combined with its own estimates of hours worked, to calculate trends in both labor productivity and total factor productivity for a variety of sectors, including construction. This metric likewise shows construction productivity as stagnant or declining. It’s not uncommon for discussions of productivity to also reference this BLS metric; for instance, it’s used by Federal Reserve economists Daniel Garcia and Raven Molloy in their 2025 paper “Reexamining Lackluster Productivity Growth in Construction”.

Sector-wide measures of US construction productivity thus tell a consistent story of stagnant productivity growth, differing only in how bad the problem appears. By some measures, productivity is merely flat over the last several decades; by others, productivity has declined significantly.

Sub-sector productivity metrics

Subsector metrics are also commonly used to get a picture of national construction productivity trends, particularly metrics that look at trends in housing construction. In their 2023 NBER working paper, “Why Has Construction Productivity Stagnated?” Princeton economist Leonardo D’Amico and coauthors looked at productivity trends in US homebuilding by dividing the total number of housing units produced in the US by the total number of residential construction employees. They found that housing productivity had declined significantly since the 1960s — though, as we’ll see, there are issues with their choice of metric. Goolsbee and Syverson also looked at housing units per employee in their 2025 paper, along with another housing productivity metric, square footage of housing per employee. As with D’Amico et al., housing units per employee shows declining productivity over time, while square feet per employee shows slightly more complex trends: productivity appears to decline between the 1970s and the early 1990s, and decline since then for multifamily construction, but single-family construction shows an increase in productivity of close to 50% between 1990 and 2020. In their 2025 paper, Garcia and Molloy also look at productivity trends in single-family home construction using square footage of housing produced per employee, though they also try to include quality adjustments in this metric. (We’ll discuss quality adjustments more later.)

The Bureau of Labor Statistics also produces estimates for construction productivity trends for four sub-sectors: single-family home construction, multifamily home construction (i.e., apartment buildings), industrial building construction, and highway and bridge construction. These are based on individual subsector estimates of construction spending from the US Census, and BLS estimates of hours worked. Per the BLS, while single-family home productivity has been stagnant since 1987 and highway and bridge productivity has declined, productivity is up for both multifamily construction and for industrial building construction.

Construction subsector productivity estimates thus generally show stagnant or declining construction productivity, though with significant variation. Some subsectors show increasing productivity, and some show different trends by different metrics. Single-family home construction shows increasing productivity when measured by square feet of home per employee, but unchanging productivity when measured by subsector spending per labor hour; for multifamily home construction, the reverse is true.

Project and building productivity metrics

Below the level of construction subsectors, we have productivity metrics that look at trends for individual building types, such as the amount of labor required to build a single-family home. These sorts of metrics are much less common, as it’s rare to get detailed project-level productivity data from builders, but are still seen occasionally. In 1964 and 1972 the Bureau of Labor Statistics conducted studies on the number of hours it took to build a single-family home, finding that the average annual percent change in labor hours per square foot was just -0.6% per year (ie: productivity increased, but slowly). The Construction Industry Institute has a “Benchmarking and Metrics Productivity Database” that tracks project-level productivity metrics for submitted projects. A NIST analysis of this database from 2000 to 2007 noted a decline in project-level productivity, measured in output in dollars per labor-hour.

We can construct our own building-level productivity metric by using data from construction estimating guides. Estimating guides, produced by companies like RS Means and Craftsman, provide information on cost, labor, and material requirements for hundreds of different construction tasks, and are used to generate cost estimates for new construction projects. Some companies have also often been producing their estimating guides for many years, making them a valuable tool for analyzing productivity trends; both RS Means and Craftsman have been producing estimating guides since the 1950s.

Starting in 1993, Craftsman’s National Construction Estimator included an estimate of the total number of hours required to build a “typical” single-family home. If we compare the estimated number of hours per square foot in 1993 and 2026, they’re almost identical. The only task that has changed is insulation installation, which took a single man six days in 1993 and now takes one man 3 days. It’s also worth noting that this hours per square foot figure is also virtually the same as the number of hours per square foot calculated by the BLS in their 1964 and 1972 studies.

Thus, project-level measurements of US construction productivity also tend to show a stagnation or a decline in US construction productivity over time.

Task-level productivity metrics

Finally, below project-level productivity metrics, we have measures that look at productivity of individual construction tasks: laying bricks, framing walls, installing plumbing, and so on. These metrics are fairly commonly used, thanks to the existence of estimating guides. We can look at changes in task-level construction productivity by seeing how the time and labor required for various specific construction tasks has changed in estimating guides over time.

Allmon et al (2000) looked at productivity changes for 20 different construction tasks from 1974 through 1996 using RS Means estimating guide data, and found that labor productivity increased for seven tasks, decreased for two tasks, and was unchanged for 11 tasks. Goodrum et al (2002) looked at productivity changes between 1976 and 1998 for 200 different construction tasks using data from several different estimating guides. They found that labor productivity declined for 30 tasks, was unchanged for 64 tasks, and improved for 107 tasks, with an average growth rate in labor productivity ranging from 0.8% to 1.8% depending on the estimating guide. A follow up study by Goodrum in 2009 that looked at productivity trends in 100 different construction tasks between 1977 and 2004 found a somewhat lower average productivity increase of just 0.47% per year, with significant variation between task categories.

We can also use different versions of estimating guides to do our own analysis of productivity trends. The chart below shows the relative installation rates for 40 different construction tasks which are listed in the RS Means estimating guides from 1985 and 2023. 10 tasks got more productive over the period, 10 got less productive, and 20 tasks were unchanged.

We can also try to calculate installation rates directly, using the values RS Means lists for task labor cost and hourly wages. The chart below shows the installation rates calculated for 17 construction tasks performed by either carpenters or sheet metal workers that were listed in the 1954, 1985, and 2023 version of the RS Means estimating guide.1 Effective installation rates for each task were calculated by dividing unit labor costs for the task by the average worker wage for that task type. By this analysis, 12 of 17 tasks improved in productivity between 1954 and 1985, and 15 of 17 tasks between 1985 and 2023 got more productive.

One challenge with task-level productivity metrics is that we should expect a major mechanism of productivity improvement to be replacing old tasks for new ones. Steel manufacturing became massively more productive with the introduction of the Bessemer process, which took much less time and effort than the previous cementation process, but a task-level analysis — seeing how productivity in the cementation process improved over time — wouldn’t capture this.

One way around this is to look at the categories of tasks necessary for completing a building, rather than specific tasks. We can do this using Craftsman’s National Construction Estimator, which includes a breakdown of what’s necessary to complete a single-family home — excavation, installing doors and windows, running wiring, etc. — and what fraction of the total cost to build a home they make up. By looking at changes in these fractions of total home cost over time, we can see which sorts of tasks have gotten more productive and which have gotten less productive.

The chart below shows the relative fraction of different categories of tasks needed to build a single-family home for 1986 and 2026. Overall, there’s surprisingly little change: most task categories have the same ratio of overall costs in 1986 as they were in 2026, suggesting few types of tasks overall had much change in productivity.

Overall task-level productivity analysis shows significant variation in productivity trends. Looking at published installation rates for several dozen construction tasks between 1985 and 2023 implies that, as with other measures, construction productivity has shown little to no increase. Looking at high-level tasks needed to complete a single-family home also shows few tasks that have improved in productivity. But other analyses yield different results. Calculating implied installation rates using labor costs suggests significant task-level productivity improvement over time. Likewise, various studies of installation rates show construction tasks improving in productivity on average from the 1970s through the 1990s (with rate of improvement perhaps falling off over time).

International productivity measures

The above metrics of construction productivity all look at trends in US construction. However, it’s also worth understanding construction productivity trends in other countries. If other countries show substantial construction productivity improvements, that suggests that the US’s productivity challenges are something specific to the US. But if other countries show stagnant or declining construction productivity, that suggests the challenges may be due to broader trends, or to the nature of the process of construction itself.

We can look at international trends in construction productivity at the sector level by using KLEMS databases, which aggregate industry-level productivity data for countries around the world.2 EU KLEMS has productivity data for European countries, as well as the US, UK, Japan, and (for older releases) Korea, Canada, and Australia. Asia KLEMS has productivity data for Japan, Korea, Taiwan, and India. LA KLEMS has productivity data for several Latin American countries, and World KLEMS has links to Russian, Chinese, and Canadian KLEMS data.3

The charts below show changes in construction labor productivity, measured as gross value add per labor hour, for 45 different countries. Productivity has been normalized to equal 100 for the first year in which there’s data.

Per KLEMS data, US construction productivity steadily declined from 1970 to around 1995, after which it leveled off. This is broadly consistent with other measures of US construction-sector productivity, which show either stagnant or declining productivity since roughly the 1960s.

Other countries show a somewhat different historical pattern. For the 20 countries where data goes back to the 1970s (which includes most of Western Europe, the Anglosphere, Japan, and Korea), only one other country, Greece, shows declining construction productivity from 1970 to 1995, and its rate of decline is much lower than the US. Every other country saw rising construction productivity during that period.

Since 1995, however, construction productivity in these 20 countries (minus Canada and Korea, whose time series stopped around 2010) improved much less. Per KLEMS, the US has an average annual rate of improvement of 0.2% per year from 1995 to 2021, which is slightly better than average for this group of 18 countries. Only Belgium and Ireland have maintained a steady, high rate of construction productivity improvement greater than 1% per year.

Starting in the 1980s, there is also KLEMS data for China, Taiwan, and India, and starting in the 1990s there’s data for Eastern Europe, Latin America, and Russia. Taiwan shows improving productivity until around 2000, after which it flattens out/declines. Korea and Russia show similar patterns of improvement followed by stagnation. India’s productivity improvement has remained flat, as has Poland’s, Czechia’s, Malta’s, Cyprus’ and Slovenia’s. Other Eastern European countries have improved in construction productivity since the 1990s, as have Latin American countries (with the exception of Honduras, which has declined significantly over time).

China’s productivity improved from the late 1980s through the 2010s, though its rate of improvement does not appear to be particularly impressive. (It’s roughly similar to the historical rates of improvement seen in Korea, France, Sweden, or Portugal.)

Goldman Sachs also looked at international construction productivity for several large, wealthy countries in a 2026 report. While they also found poor records of construction productivity for most countries since 1990, per their analysis the US had the worst record of construction productivity improvement of any country analyzed. This appears to stem in part from using BEA data for US productivity calculations, which yield a greater productivity decline than other US productivity metrics.

Overall, international construction-sector productivity data suggests that the US is not alone in suffering from stagnant or declining construction sector productivity. Rates of productivity improvement in the US over the last several decades appear broadly similar to improvement rates observed in other large, wealthy countries. Many countries that at one point had substantially improving construction productivity (Western Europe, Korea, Taiwan) have seen it flatten out in recent years. Others (India, Japan) have never seen substantial improvements. The countries that do show sustained, large improvements tend to be either small (Ireland, Denmark, Estonia), poor (Colombia, Peru), or both. Rates of construction productivity improvement are nearly always much lower than improvements seen in manufacturing, or in the economy overall.

Challenges with measuring construction productivity

Accurately measuring trends in construction productivity means accurately measuring both inputs and outputs over time. There are a number of difficulties in doing this.

For outputs, one major challenge is that outputs might change over time in ways that are difficult to account for. Sector-wide measures of construction productivity, for instance, typically measure construction output in terms of total construction spending, tallying up everything that was spent on construction during the year — housing in Texas, skyscrapers in New York, schools in Washington, and so on. However, if the composition of things that are built in the country changes — if over time there are more homes built in Texas and fewer skyscrapers built in New York — this could distort productivity measures.

For example, assume there are two types of houses, Type A which requires 1000 hours of labor to produce, and Type B which requires 1500 hours of labor to produce. Last year 100 of each type of house were built, yielding 200 total houses built with 250,000 hours of labor. The next year, however, 50 Type A houses and 150 Type B houses were built, yielding 200 total houses built with 275,000 hours of labor. If you simply look at the outputs (200 houses each year) without accounting for the differing difficulty of building them, this looks like a roughly 9% decline in productivity, since it took more hours to build the same number of houses. But what’s actually happened is a shift to building fewer easy to build houses and more hard to build houses. You could in fact get a measured productivity decline even if productivity was improving for each type of house. This is a variation of Simpson’s Paradox, the observation that for groups with differences between them, trends in individual sub-groups can be reversed when looking at the groups collectively.

These effects of a changing output mix aren’t merely theoretical. When Allen (1985) looked at US construction productivity trends from 1968 to 1978, he found that this sort of change in the output mix — specifically, a shift from capital-intensive civil construction to labor intensive home construction — was responsible for the lion’s share of the measured productivity decline.

This sort of shift in the output can also be at work in sub-sector measures of construction productivity. Measures of housing sector productivity, for instance, can be distorted by failing to account for changes in what sort of housing gets built. D’Amico et al. (2023) used “housing units per employee” as a measure of construction productivity, but this measure fails to take into account the fact that on average houses increased in size over time. An average home in 2025 is much larger, and requires more effort to build, than an average home in 1985.

This particular distortion is relatively easily corrected by multiplying the number of homes produced by average home size, to get “square feet of home produced per employee”. As we’ve noted, several studies of construction productivity use this metric. But increasing size isn’t the only way that homes change over time. For one, modern homes are built to stricter building code standards than older homes; they will have greater fire resistance, greater ability to withstand high winds and earthquakes, and greater energy efficiency. For another, modern homes have more amenities and services in them: they’re more likely to have air conditioners, dishwashers, insulation, generally will have more bathrooms, and so on. Thus, a square foot of home built today should be considered as more output than a square foot of home built in 1960.

It can also be challenging to accurately measure inputs to the construction process. Labor is the construction input most often tracked, but this can be subject to its own “input mix” problems — namely, ensuring whether labor hours are all actually being devoted to the outputs being considered. As with changes in the output mix, if there’s a shift in how construction workers are spending their time, this could show up as a change in construction productivity that’s not actually occurring.

For instance, we’ve noted that both D’Amico et al (2023) and Goolsbee and Syverson (2025) include productivity metrics which track the amount of housing produced per residential construction employee. However, workers in residential construction don’t merely build new houses, they also renovate old houses. And there has been a gradual trend upward in spending on residential renovations. Renovations now represent 40-45% of spending on residential construction, up from 20-25% in 1970. Thus some of the measured change in “housing per employee” is likely an artifact of employees increasingly working on renovations. (Goolsbee and Syverson’s housing-per-employee metric likely doesn’t have this problem for post-1990 data, as after that BLS employment data breaks down residential remodeling employment separately).

More generally, if labor isn’t properly accounted for, that will obviously distort any productivity measures. It’s notable that for some of the sub-sector productivity measures produced by the Bureau of Labor Statistics, labor hours worked appears to be much more uniform than changes in output. For industrial buildings, there are several spikes in output (2009, 2015, and 2024) during which labor input stays flat, resulting in productivity spikes. It’s possible these are real (though it seems unlikely that firms suddenly got 50% more productive, then 50% less productive, over just a few years), but it’s also possible these are fictional, at least partially the result of labor inputs not being properly accounted for. Notably, single-family home construction does show labor inputs rising and falling in concert with output, and shows productivity as much flatter over time.

Another problem regarding labor is that many measures of construction specifically measure labor productivity — output per labor hour, or per employee — rather than total factor productivity (output per total amount of inputs). This is a problem because it’s often possible to automate or mechanize construction work — replace labor with capital — in ways that aren’t efficiency-enhancing. Construction automation and mechanization often requires a large amount of equipment to duplicate what’s possible with a relatively small amount of labor. (When I worked at the modular construction startup Katerra, the executives would often complain how hard it was for Katerra, with its expensive factories, to compete with “Bubba and his truck”: low-overhead contractors who used little more than power tools and manual labor.) Thus labor productivity could improve even as overall productivity declined.

A problem related to changes in the output mix is that construction output is often measured in dollars spent. Spending in dollars must be adjusted to account for the fact that the value of a dollar changes over time. This is typically done using what’s known as a deflator, some measure of price changes over time that can be used to convert spending in dollars to a consistent measure of construction output. The Consumer Price Index (CPI), which measures price inflation for a basket of consumer goods over time, is an example of a deflator.

There are several challenges with using deflators. One is simply choosing one that accurately captures price changes relevant to construction. Construction uses certain sorts of inputs whose price changes may not be adequately captured by commonly-used deflators. A spike in the price of building materials (such as was observed during the Covid-19 pandemic) might dramatically raise the costs of construction much more than the CPI rises.

For example, consider a building that requires $100,000 worth of materials, and gets sold for a threefold markup at $300,000. Now an identical building is being planned, but building material prices double, raising the input price to $200,000 and the output price is now $600,000. However, the Consumer Price Index only increases by 10%. Deflating output by the cost of building materials would show identical output for the first and second buildings — the price of the final building doubled, but so did the cost of the input materials. But deflating by the consumer price index, which only rose 10% between the first and second buildings, would now make it appear that the second building was worth much more output than the identical first building.

In practice, this sort of disconnect between construction input prices and other measures of inflation appears rare — in Teicholz’s 2013 article, there was little difference in productivity trends using construction-specific deflators and the more general Consumer Price Index. A more difficult problem with deflators is that a deflator can mask any gains (or losses) in productivity. Ideally we would have a deflator that measured changes in the prices of a finished building — a so-called “output deflator”, which measures the price changes of final goods and services. The Consumer Price Index is an example of an output deflator. However, many construction deflators are “input deflators”, which track changes in the price of various construction inputs — materials, labor, and so on. Using an input deflator can mask changes in productivity, because actual output is not being properly accounted for.

For instance, assume we have a building that requires $100,000 worth of materials, and 1000 hours of labor to build. We sell this building for $300,000. Now suppose that building material prices stay flat, but we figure out a way to build an essentially identical building for $75,000 worth of materials and 750 hours worth of labor, which we sell for $225,000.

In this example, productivity has improved markedly — we’ve gotten an effectively identical building for 75% of the material and 75% of the labor required previously. However, if we used an input deflator, we’d get no measured change in productivity. Output is measured in dollars for both buildings — $300,000 for the first, $225,000 for the second — and the value of the deflator stays the same since input prices haven’t changed. So using an input deflator would make it look like we’re using 75% of the material and labor inputs to get 75% of the output. Conversely, if we got less productive — if it now took $125,000 worth of materials and 1250 hours of labor to build an effectively identical building which we sold for $375,000 — that would also look like productivity being flat, getting 25% more building by using 25% more materials and labor, rather than the decline that it is.

It’s thus important to have some deflator that can capture changes in the actual value of buildings, not merely the prices of their inputs. However, figuring out how much a given amount of construction should be valued is difficult: it’s susceptible both to the output mix problem (a sector-wide deflator needs to account for the fact that the mix of buildings may be changing), and the changing quality problem (your deflator needs to somehow capture the fact that a 1000 square foot house today is “more house” than a 1000 square foot house built in 1975 due to code improvements, more amenities, and so on).

Thus “what deflator to use?” is a perennial problem when analyzing trends in construction productivity. In his 2013 article on construction productivity, Teicholz went so far as to use seven different deflators to analyze construction productivity trends. Goolsbee and Syverson (2025) noted that much of their apparent measured decline of construction productivity was a product of the construction deflator used by the BEA, which showed a much higher rate of increase in construction prices than other deflators: since output is dollars spent divided by the deflator, this would register as a decline in construction output, and thus a decline in productivity, compared to deflators which showed a lower increase in price. Garcia and Molloy (2025) note that the Census Single Family Price Index, a commonly used deflator for single-family home construction, does not fully capture changes in home quality over time: the quality adjustments include things like increases in square footage and changes in HVAC systems, but not things like improved energy efficiency or interior finish quality. Garcia and Molloy estimate that improperly accounted for quality changes result in underestimating single-family construction productivity improvements by up to 0.8% per year.

More generally, the accounting required for accurate sector or sub-sector construction productivity estimates is very difficult. We can see this by looking at changes in KLEMS data over time. New KLEMS releases don’t merely extend the time series for existing data, but they revise and update past data. These revisions can substantially alter productivity trends. Between 2019 and 2024, revisions to the UK KLEMS data resulted in a swing from showing positive construction productivity growth between 1996 and 2016 to those same years showing negative productivity growth. Swedish data revisions showed the opposite, going from negative productivity growth using 2019 data to flat productivity growth using 2024 data.

Task-level metrics of construction productivity are immune to many construction productivity measurement problems. Because we typically have a direct measure of output (materials installed per hour, etc.), we don’t face the problem of converting an output measured in dollars, or of output mix problems that stem from combining different types of outputs into one measure. But task-level metrics have their own challenges. For one, we face the problem of how to go from task-level productivity estimates to estimates of whole building, subsector, or sector productivity. For instance, task-level productivity may be improving on average (as per Goodrum et al. 2002 and 2009), but perhaps the productivity-improving tasks are less commonly used, and the more commonly used ones are showing less growth. It seems notable that for Craftsman’s full-house estimates, only one category of task — insulation installation — improved in productivity from 1993 to 2025.

Another difficulty of task-level measures of productivity is that they’re almost universally based on estimating guide data, rather than data from actual buildings produced. But this is a relatively minor weakness, as there’s good reasons to think that estimating guide data is reasonably accurate: empirically, it’s valuable enough for construction businesses to continue to pay for it over many decades, and estimating guide data seems to largely track data from other sources. Potter and Syverson (2025) noted that RS Means estimates of city-level construction costs largely agreed with construction cost survey data, and Craftman’s task-based estimates for single-family home construction cost aligns with the average price per square foot of new home construction from the US Census.

Overall, it’s hard to be confident of any single metric of construction productivity, due to the numerous, difficult-to-resolve measurement issues at work. Examinations of construction productivity will thus often use multiple metrics. Goolsbee and Syverson (2025) consider several different productivity metrics, and Teicholz (2013) uses several different deflators to try to avoid distortions from any outlier deflators.

Conclusion

Tying this all together, what can we say about trends in US construction productivity?

We can look at trends in productivity — the amount of output we get for a given amount of input — at different levels, from the sector as a whole, to sub-sectors such as housing construction, to individual buildings, all the way down to individual construction tasks. Productivity metrics for the entire construction sector consistently show productivity either staying the same or declining over time, in contrast to other sectors like manufacturing and to the economy overall. We see these trends both in the US and in most large, wealthy countries.

Sub-sector productivity metrics also broadly show stagnant or declining productivity, though not universally. Project and building-level measures of productivity also generally show trends of stagnant or declining productivity, though most of this data is for home construction.

Task-level productivity trend estimates are, like sub-sector trends, somewhat mixed. Some task-level estimates show similar trends of stagnant or declining productivity over time, others show sluggish to modest growth depending on the collection of tasks and the time period considered.

All these estimates must be taken with a grain of salt, as it’s difficult to accurately measure construction inputs and outputs. Productivity estimates can be distorted by a variety of factors, including changes in the output mix, failing to properly account for construction labor inputs, and improperly deflating construction spending.

But these measurement difficulties are tempered by the fact that the estimates almost all point in the same direction. Most measures of construction productivity show at best very low levels of growth, far below what’s observed in the economy overall; many measures show declining productivity. We see some subsectors that may have seen periods of substantially increasing productivity (such as industrial building construction), and evidence that some individual construction tasks have gotten more productive for at least some periods of time, but overall the picture of stagnant productivity growth is fairly consistent.