Broadsheet

Today we will pour one out for the vaunted technical interview process, which is on its last leg. And we’ll talk a little about what’s replacing it.

This post has been almost 35 years in the making; that’s how long I have been conducting technical interviews. And for a few of those decades, I also worked to try to improve the process itself. I’ve had to care a lot about it, because it’s so broken.

It turns out interviewing was broken long before I learned the trade, and despite the many attempts to band-aid it, it’s still broken today. It has managed to survive in spite of that. But it is finally dying on its own. People are a bit unclear on what’s next, so we’ll talk about some of our options.

But it’s not an easy path I bring you, no silver bullet. Remember that, grasshopper, when you get to the end and come back to yell at me.

The Two Big Dogs

Back at Amazon, we had this elite group, the Bar Raisers, which I hear is similar to a role at Microsoft they call As-Appropriate (AA). In both cases, a trusted interviewer (the BR/AA) is assigned to every interview loop, and has the power to veto any unqualified people the interviewers try to sneak through. I was a BR, and also part of “Bar Raisers Core”: a small group Bezos and Dalzell tasked with defining the BR role itself, choosing new bar raisers, training them, and reporting on the program’s efficacy.

The BR and AA roles are a tacit acknowledgement that you can’t trust your interview teams to make good hiring decisions. Which even more broadly, suggests that if every single interview loop needs a babysitter, then it is a flawed process. But we were doing our best.

Of course we don’t frame it as babysitting, naturally. We cheerlead and rah-rah about keeping a “high hiring bar.” And BR/AA aren’t the end of it. There are tons of other interview process band-aids, all variations on how you conduct four to six interviews in a single day. Everything revolves around fitting the assessment into a single round or two of interviews. That part never changes.

None of these band-aids really help — we still all hire tons of false positives (unqualified) and turn away false negatives (actually qualified), despite every attempt to make the process perfect, or even good.

The reality is that talent evaluation, a corner of our industry that hasn’t changed much in five-ish decades, is embarrassingly busted. It just doesn’t work that well in practice.

The people who know this best, and who feel it most day to day, and who ultimately have the least power to change it, are all in HR. The tech side of the tech industry doesn’t want to change it because of inertia: the process has worked just barely well enough all these years to resist an overhaul. So all HR can do is show us how bad it is, and then try to do damage control when it fails. I’ll share an amazing story about this in the next section.

After Amazon, I kept on trying to improve the interviewing process while I was at Google. I spent years on Google Kirkland’s “Hiring Committee”, where we processed thousands of Microsoft résumés, would-be escapees from over the hill in Redmond. I published a 30-page internal résumé screening guide. I even wrote a blog post, Get That Job At Google, which is still handed out by Google recruiters, seventeen years later. I took this stuff pretty seriously back then!

In short, there is very little that I don’t know about the tech-assessment business. And I’m about to pull a Kitchen Confidential on it.

This post comes with a diagnosis and a prescription. But the important part is the diagnosis. I want to convince you that we need a radical change in how we assess people, and that tech interviews are on their last leg.

Let’s see how well I do. As for the solution, we can figure that out once we agree that it’s a problem.

Remember That Time We All Fired Ourselves?

The outcomes from interviewing are statistically terrible. Google did wave upon wave of analysis over the years, and all the results were incredibly depressing.

To name just a few off the top of my head: interviewers barely agreed with each other. Put the same candidate in front of two of our sharpest people and you’d routinely get a confident “strong hire” from one and a flat “no” from the other. There’s no oracle interviewer, not even Jeff Dean.

And once people were actually on the job, their interview scores told you next to nothing about how they’d do — at least the way we ran the loops. Hell, some of our star performers failed their Google interviews four or five times, finally got in after 2+ years, and then outshone everyone else.

Interviewing, it turns out, is a big game of darts. A “do I like you” dating round.

All we could find at Google, from all the statistics, is that there are a bunch of horrible unconscious interviewer biases keeping us from hiring people who ought to be working there. Google never published the results internally and I just got to hear rumors from HR friends, but yeah it was pretty bad.

Gergely asked me about the tech-assessment situation in one of our podcasts, and I told him a story that I think is relevant so I’ll recount it here. You can hear the longer, funnier version over on his podcast.

At Google, rather than putting a little black desk on every interview loop, they just assumed all the interviewers had their heads up their arses (an evidence-based assumption), and they formed a committee at each site called Hiring Committee (HC). This committee acted as the final arbiter and gatekeeper on all hiring for the site. And I was on it for quite a few years.

Our HC group at the time was roughly fifteen strong, and included many local powerhouse Googler colleagues, including co-inventors of huge technologies, authors of interviewing book series, and people who are now very senior leaders.

We felt like we knew our shit.

One day, the recruiters gave us a special round of packets to review. In these special packets, we were able to read the interviewer notes and candidate responses. All personal details were stripped out, and we were told it was a “calibration exercise.” We had to do our regular voting job with these special packets, and see how it went. I think we may have assumed they were from another site, since cross-site calibration was common.

Our group did our job, and voted not to hire about 2/3 of the packets. This was about par for the course.

But surprise surprise, this time, those were our own packets from when we had all interviewed at Google. The recruiters had tricked us into reviewing our own interview packets, and we had voted not to hire most of our own group.

For that brief moment, we all had a glimpse into how utterly broken our process was. The people-team had rubbed our noses in it.

But we never fixed it.

Change is Taboo

I once challenged the interview process at Google in a public mailing list, and an Important Eng Leader took me aside and told me I’d “farted in church.” People are not allowed to question whether the interview process is valid. Challenging it is akin to casting aspersions on the entire engineering staff. First of all, how dare you?

Moreover, many engineers and managers continue to defend the process, even knowing how broken it is, because it’s a gauntlet that they passed, so others must, too. Which sounds very silly when I say it like that, but you hear echoes of it whenever you suggest changing the process.

When you add it all up, and putter around with it for about 35 years, you ultimately find that interviewing, which aims to determine “can this person do this job,” is fundamentally unable to answer that question with any degree of reliability. It’s bordering on pseudoscience.

Bravo to Google for at least trying to measure it. They did find some actionable stuff. For instance, any more than four interviews and you’re just playin’ with your food. Stuff like that. But overall it was a bleak and mostly non-actionable picture.

Most companies never introspect at all on their own interviewing processes — at least, not in any real “we’re aiming to change this” sense. They might shuffle around which competencies get covered, or what the debrief meeting might look like.

The fundamental process — a short series of 1-hour interviews — hasn’t changed in fifty years. We have all been using the same stupid, broken interview process since before most of us were born. Dunno about you, but I’ve had enough of it.

So in today’s blog post, I’m going to show you everything that’s been tried, then point in the direction of a better way. And as an industry, we can start moving in that direction. As long as enough of us are doing approximately the same thing, it should gain traction.

I believe that within a few years we will mostly stop conducting technical interviews, and they will fade into a cute historical footnote like phrenology (which I would not be shocked to hear some interviewers still use). Interviewing will become just another somethin’ weird we used to do.

You ready? I am. Let’s goooo!

The Signal Problem

Talent assessment is a problem of signal. You need a lot of it. You want to try to paint an increasingly clear picture of the candidate, and that takes a lot of data.

The first signal source you get is a candidate’s résumé. “A résumé,” as Dave Barry tells us, “is much more than just a piece of paper. It is a piece of paper with lies written all over it.” Companies get thousands to millions of résumés per year, and the signal-to-noise ratio is awful. AI-assisted writing is exacerbating the problem, and résumés have become all but useless as a signal today.

What we used to do first was a technical phone screen. They were super hard, for both sides. Everyone hated them. Imagine writing code for someone with only audio. Zoom made them obsolete. But even those awful phone screens were still a much better signal than a résumé, because you get to spend time with the person.

The next level of signal is “work completed”, which includes credentials and assessments like coding academies and challenges, as well as the person’s OSS work. They are both weak signal sources because you don’t get to work with the person while they’re doing it. But they can be a useful tie-breaker or foot-in-the-door signal that boosts you to the next evaluation stage.

The hiring signal doesn’t start to improve dramatically until you get to on-site interviews. But even in-person interviewing is infamously unlike real-world work. How does interviewing at a whiteboard compare to working with them in real life? I think we all know the answer to that.

So you get a few minutes with their résumé, a half hour video screen, and a handful of in-person interviews. Today’s standard hiring process only involves collecting a few hours of signal, in order to make a decision that could last years.

Provisional Employment to the Rescue

The gold standard talent-assessment signal, when available, is an internship. This situates the candidate as a quasi-employee for typically around 3 months. They are part of a team, they get “real” work — as real as the team can make it, which varies — and they are evaluated on how well they complete that work.

A 3-month internship is self-evidently a much stronger signal than a one-day interview loop. Chances are, unless you really drop the ball on managing the internship (it’s easier to mess up than you’d think), then you’re going to have a much clearer picture of their skills and capabilities, and how well they “mesh” in your team, than you did at the start of the internship. The signal is so much stronger than the interview loop that you often ignore the interview feedback when making the final decision. It was just what got the intern their foot in the door, and the internship itself turns into the assessment.

I’ve seen companies go even further than an internship, in their quest to find the Right People For The Job. The first company I worked for, Geoworks, had a strict requirement: every single candidate had to complete a six-month co-op, before they would make the hiring decision.

The co-op resembled a summer internship, but we were highly paid full-time employees (with benefits for anyone who didn’t have them from school), doing hardcore real work for twice as long, all of us desperately wondering if we were going to get hired at the end of it.

There is a material difference between the signal from an internship (~7 weeks of usable work time after ramp-up) vs a co-op (5 months actually working). That year, 1992, Geoworks hired 3 out of 8 of us. Crazy high bar, it felt like they were making new Witchers. Sad for the ones who didn’t make it. But their approach produced a company of nearly all stars and superstars.

As evidence of the co-op’s effectiveness, Amazon acqui-hired the Geoworks Seattle office in 1999, a few months after I joined, bringing in nearly 30 engineers (I kicked off the process myself, and it was surreal to see my old coworkers wandering the halls a few months later.) Amazon leaders have said many times, and I’ve even heard it from an old-timer VP as recently as two weeks ago, that the Geoworks group was by far the strongest group of people they ever brought in. Many Geoworkers went on to do incredible things at Amazon and/or Google.

So the Geoworks 6-month co-op is platinum-level signal. Can you go further? How far are you willing to go? On the supply side, the University of Waterloo famously sends their Computer Science students through a total of six internships, giving them roughly 2 years of real-world work experience before they graduate. This boosts the signal out of Waterloo, and companies compete fiercely for Waterloo interns and grads above all other schools.

What we’re talking about here is provisional employment. It’s lazy evaluation before binding the “hire” variable. It doesn’t matter if it’s an internship, a co-op, or a contract-to-hire, it’s a temporary position, not a part-time one: Provisional employment is a different axis from fractional employment. Provisional employment is currently the best solution the industry has found for the problem of knowing reliably who to hire, before you hire them.

But wait, if provisional roles are so effective, why bother with making them provisional? Why not just hire people, put them to work, and fire them if they don’t work out?

If only it were so easy. Actually, it is, except for, you know, The Law. Most provisional employment schemes are workarounds for U.S. employment law, which, believe it or not, affords employees some protections, even if those protections are weak compared to other parts of the world.

In the U.S. you generally can’t just up and fire someone, even if they are underperforming, even if it’s at-will employment, without risking a lawsuit and some sort of cash outlay. This is what makes hiring decisions so high-stakes.

But if the employee is provisionally employed, well, that’s a different matter. Their internship or contract is just “over.” Thanks for playing.

So in situations where the company can get away with it, they can and absolutely do use provisional roles to defer making permanent role decisions for as long as possible.

You call this a rescue?

OK, then! If provisional employment is the answer (it’s not, but let’s pretend it is for the moment, since it’s best-of-class for now), then why aren’t more companies requiring internships before making their hiring decisions?

The reason is, hiring engineers has historically been so competitive that you couldn’t convince a senior engineer to do an internship. College kids might have all the freedom in the world, but adults have families and mortgages and need steady employment. They did not want to sign up for a job they might not get to continue after 3 to 6 months. A job where they’re actually being constantly evaluated for the go/no-go decision. It’s a no-brainer that they’ll go elsewhere.

So the industry converged on not requiring it.

That was the old equilibrium, and it only held because good engineers always had somewhere better to be. That assumption is starting to wobble. I won’t belabor what I think you all can already feel coming, but if a sizable slice of the skilled-labor pool ends up “between gigs” at the same time, there may be no elsewhere for them to go.

At that point, if there are enough people on the market, try-before-you-buy is no longer an insult that you’d never dare float to a senior engineer. It’s a foot in the door that they’ll be glad to have.

Today’s hiring process is heavily optimized, and it’s going to be hard to change. I suspect this is why we are seeing so many tweets from founders firing their HR teams. HR is designed to keep things compliant with laws that are struggling to keep up with the pace of change, so they’re admittedly in a tough spot. Just be aware that if you want to make radical changes to your hiring process, there will be strong pushback from all corners of your org.

In spite of that pushback, I see some forces appearing on the horizon which are converging to squeeze the system, after a half a century, to its breaking point.

We’re seeing the first breakdowns at the fringes — offline assessments poisoned by AI cheating, online interviews turning out to be fronts for sanctioned North Korean IT workers, résumé floods that cannot be dealt with in any reasonable way. The system is undergoing tremendous strain that it was never designed for.

There’s an even more fundamental breakdown happening, which is the rate of change of the shape of knowledge-work roles. Hiring used to be something that changed very slowly. Every ten years there would be a revolution: internet, mobile, cloud, and people would have to learn some new stuff, and update job descriptions. But for the most part, the fundamentals of computer science, software engineering, project management etc. have remained stable for decades.

This meant that it was pretty easy to do long-term planning for what kinds of questions you want to ask people, what sorts of things to evaluate candidates on, and how much faith you can put in the results. But that has changed almost overnight, and it continues to change pretty rapidly. We no longer know what job roles to post, what questions to ask, nor how to evaluate candidates properly using any of our existing processes.

So. It ain’t working. And yet we’re still not talking about what it will take to change it. Because it’s unthinkable. It’s farting in church.

Let’s talk about what a real rescue would look like.

Let’s Stop Simulating Work

Every step of the interview process is an elaborate attempt to simulate actually working with someone. Reading their résumé, you close your eyes and imagine what it would have been like to get all those Ph.D.s. They must be insufferable. Moving on! Then there’s the phone screen, where you try not to imagine them wearing a gorilla suit, and usually fail. Then the interview, where you learn how they actually smell.

Whiteboards, made-up design questions–it’s all just theater, trying to act like it’s real work.

We’ve already established that the gold standard of assessment is working directly with someone on real work, in a real environment, for as long as needed to make the call.

So the short answer is, stop simulating. Post real pieces of work, let the candidate do it, look at what they actually produced, and decide from that work.

We never did this before for all sorts of reasons. It’s a lot of work to carve out work. Hard to get people the credentials they need, etc. Carving up work might be easier with AI now in some cases, but still pretty hard if you’re a locked down enterprise.

Another reason is that on the supply side, nobody wants to sign up to do a bunch of free work just to be rejected. If you just put up work, the candidates incur all the risk, meaning they walk away with nothing if you don’t hire them. You can pay them, which helps soften it, but they’re still looking for full-time work that you might not offer them in the end.

So until very recently, the idea of using real work as an assessment tool has encountered too much resistance on both sides of the interview table.

Campfire Is The New Interview

If you spend any time doomscrolling on X, the new meta in SF is “come work with us for a few days.” Paid, real codebase, real ticket, real team. From what I’ve heard, it generates the best signal people have ever seen, by a mile. I don’t think it beats a six-month co-op, but it’s a hell of a lot better than a standard interview loop.

So there’s already a movement away from traditional interviewing, which is why I feel comfortable making the claim that it’s already starting to die out on its own. It’s being replaced by bringing people in for longer stints. I’ve been calling this working model the campfire: pull up a log, build something together, see how it feels. Bringing in outside contributors is pretty easy, as they’ll have agents and should be able to come up to speed very quickly. If they can’t, there’s your signal.

But even though people are experimenting with it, the way it’s done today is a mess. Every shop is reinventing a bespoke solution. And it’s unportable: candidates do brilliant work in a trial, they don’t get the offer for reasons, and the signal evaporates. The next company they campfire-interview at starts them at zero.

Count Work Twice

Big companies all have mountains of work. Heck, everyone has mountains of work. And most companies are engulfed in a forest fire of résumés. If you can carve real pieces off your work mountain, and connect them to that fire, you can make a campfire model that scales.

Great for you, you get outside help with your work mountain. But how can it be made fair for the candidates contributing work? Easy: You make the work count twice. Once for you: you get strong assessment signal, and some real output from candidates that you will often get to keep, or at least use as a starting point.

And then, importantly, each work item also counts once for the candidate: they walk away with a permanent, portable record of what they did and how well they did it, signed by you, whether or not you make an offer.

When candidates get to walk away with something of lasting value that they can keep forever, something that may even compound over time, it no longer feels like “six hours of free work to get rejected.” It feels like a six hour investment in a permanent small reputation bump. That’s infinitely better than a flat rejection.

Make Interviewing A Profit Center

Interviewing has always been a cost center. It costs engineer-hours to produce a yes or a no, which is why people have resisted increasing interviewing time to improve the signal. It’s already considered a lot. But if you run things campfire-style, it potentially flips into a profit center. And it can pay for itself twice: once in real work shipped, and once again in something else you could probably use more of, which is gravity.

Every stamp that you hand out, pass or fail, leaves a candidate richer than they showed up. This attracts strong candidates to you, because even your rejections are worth something to them. Stamp honestly and generously enough and you stop rooting around through a trickle of identical AI-polished résumés, and start standing up a reserve: people who’ve done real work with you, can prove it, and will pick up when you call.

But they can also walk away, and take their record with them.

This system is basically worth exactly what your honesty is worth. If you hand out gold stars to everyone, you’re just reinventing LinkedIn endorsements, which are worthless. Companies whose stamps mean anything will be the ones known for only handing them out for quality work. Being a hard, fair judge is an advantage for everyone.

Where Do the Stamps Originate?

Every rideshare platform already mints the kind of record we’re talking about here: star ratings, badges for this and that, rides completed, Grab and Uber have all of it. The one thing wrong with it is that it’s trapped on each platform and disappears if the driver leaves. The whole trick to reinventing talent assessment is letting the workers carry the assessments out the door.

I’m aware of some solutions evolving in this space, both commercial and open-source, and I’m excited to share more when I can. Candidly, I wanted to hand you a coherent solution today, and through a combination of running out of space, and Opus 4.8 being way too goddamn critical of everything all the time (jfc), I decided I’d hold it for now. We’ll all just have to start experimenting.

But now you have the shape of it. Please, treat any prescription I’ve made here as a sketch. Mostly I just want you to come around on the diagnosis, and agree that we need to fix it properly for the first time in 50 years. Maybe you conclude that everyone has to require six-month co-ops. I don’t care. As long as it’s not more of the same. I just wanted to move the Overton window a bit.

Personally, I think campfire is the most intellectually satisfying solution I’ve heard as a viable replacement for our aging interview infrastructure. Hope to see you at a campfire soon!

Over the last few months we’ve examined the extent of the construction industry’s productivity problem. We’ve looked at a variety of construction productivity metrics, both for the US and for countries around the world, and found that construction productivity almost always rises much less in construction than it does in industries like manufacturing; often, it doesn’t improve at all. We’ve analyzed trends in construction costs in the US and around the world, and noted that construction almost never gets any cheaper: construction costs almost always rise at or above the level of overall inflation. And we’ve considered the most obvious strategy for solving this problem — moving the construction process into a factory — and we saw that the cost savings from prefabricated construction are frequently much less than hoped, often never materializing at all.

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

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

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

Economies of scale in homebuilding

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

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

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

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

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

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

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

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

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

Economies of scale in manufactured homes

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

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

Conventional homebuilding is subject to different building code requirements in different jurisdictions, depending on what version of the code has been adopted. But manufactured homes are built to one set of national requirements, the federal HUD code.
Conventional homes are constructed on-site, and those sites might have substantial variation between them. But manufactured homes are produced repetitively within controlled factory locations.
Conventional home construction takes place on constantly changing jobsites, with constantly changing construction crews, a constant churn that might disrupt opportunities for learning-by-doing. Manufactured home construction, which takes place within a single location, should be less susceptible to this sort of disruption.

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

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

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

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

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

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

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

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

Dollar density of various products, via The Origins of Efficiency.

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

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

What prevents economies of scale in homebuilding?

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

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

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

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

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

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

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

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

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

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

Part of the i3 production line circa 2014, via YouTube.

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

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

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

Conclusion

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

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

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

You could find sectors of construction, such as nuclear power, where the ratio of output costs to input costs is very high, and thus have more opportunities for efficiency improvements.
You could vertically integrate backwards into the production of raw materials and components, in the hopes of driving down those costs.
You could find a way to use fewer and/or cheaper raw materials.

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

The combined starts of CBUSA members would make it the fifth largest single-family homebuilder in the US.

Newer versions of the Cost Estimator have no such deductions.

Clayton Homes also produces around 10,000 other types of homes each year.

Note that I’m using this concept slightly differently than Musk. Musk’s idiot index is about the costs of the raw materials, whereas for some of these examples I’m considering the cost of purchased components as well, but the basic idea is the same.

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Man up, cowboys

What are we really talking about here?

Economies of scale in homebuilding

Economies of scale in manufactured homes

What prevents economies of scale in homebuilding?

Conclusion

Short-term, long-term impacts

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