There are few things I think about more than the essays on, and there are few with as satisfying a theoretical payout to contemplate in my orb as his essay on “leaky pipelines”, aka log-normal distributions.

The skulk: Say you’re working on a Laravel web app. You’re about 90% sure you know how to start the app. You’re 80% sure you know how to handle the infra you’ll need to get it online. And you’re 70% sure you know how to get your first customer. What is your chance of successfully going from zero to first customer? 0.9 * 0.8 * 0.7 = a little over 0.5. That’s … a lot less encouraging than any of the previous numbers, if you buy my multi-step modelling.

Software estimation is such a mess in part because it has trouble recognizing that, at least, just-in-time learning is at least non-normally distributed. Everything we know about traditional project management, from Waterfall to Gantt charts to estimation practices, are on some level based around the idea that each individual step in the chain is bell-shaped: A process taking twice as long as it normally does might be 2 standard deviations out, aka in the bottom 2.5% of outcomes. But in a log-normal distribution, processes taking two, three, or five times as long are much more common, and this throws things into disarray. Some things happen much faster than usual - but doing one estimated week-long project in half the time, and another estimated week-long project in twice the time, still leaves you in the red for your time budget.

Needless to say, it is almost never the case that in software development you know all or even most of the technical hurdles you will face in the process of development. An efficient markets hypothesis fanboy might say “If anyone already knew how to do it, it would already be done by now”. I’m not that optimistic, but I do think this leaky-pipeline approach can shed light on some more counterintuitive things about our industry.

Such as the relentless emphasis, not only on relevant experience, but on knowing your specific tooling when you apply to a given job. You and I might be hardcore enough computer geeks that we can pick up any language and be productive in it within a few weeks, but if that doesn’t hold for the median software developer, then employers really are justified in deciding that e.g. they only want to hire Java devs with previous experience in… Java. If there’s a 20% chance it takes 1 month, 20% for 2 months, … 20% for 5 months before your non-Java-background Java dev has enough experience to finally start contributing to the Java codebase, then yeah, it makes a lot of sense not to want to gamble on that. Not only do you face the possibility of paying a five- or six-figure sum for someone who’s useless, you have very little ability to estimate as an employer yourself when they will become useful.

Now what’s really interesting about this theory is that it suggests that business processes that are normally-distributed are the exception, not the norm, in a sense. Every new process a person has to undertake will have at least some phase which is dominated by learning. That’s as true of web dev as it is of learning to operate the drive-thru at McDonald’s. It takes a special kind of rigor to transform that into an activity routine and repeatable enough that you get a long tail of normally-distributed, profitable activity. Again, this makes sense: I’ve been building Django and Hugo websites and web apps for a few years now, and I’ve been using Python much longer than that, and I sort of have an intuitive sense by now for how long either of them would take me. That’s not because I’m some genius savant - it’s because I have already flowed through the leaky pipeline enough times to know how I like to approach most things. The instant I’m in new territory, we’re back to the leaky pipeline.

It may be the case that academic, just-in-case learning is less this way, because the actual things you need to learn is carefully plotted ahead of time for you. This would explain the popularity of tutorials online, as ways to tamp down on the worst-case scenario where that thing you thought would take you a week to do ends up taking you a year. That being said, I definitely had some classes in college that unexpectedly took way less time to me (wireless communication, DSP) and some that unexpectedly took way more (Maxwellian electromagnetism).