The author uses "nonlinear" but means "superlinear", because I guess sublinear cost behaviors don't cause the same kind of dramatic problems, and so we can get away with not thinking about them most of the time. I think it says something about the ways that software systems give rise to a particular flavor of problems, that allows us to (relatively effectively) ignore sublinear cost behavior, whereas in other areas those would be really important dynamics.
Sublinear costs seem like something that you'd be best off delaying to fix, maybe forever, since their impact lessens the longer you wait.
Maybe this would apply when a major change will happen that would obviate the need for a fix: a planned building teardown causes a needed roof repair on the old building to have sublinear costs, or introducing a new subsystem that eliminates the old subsystem that had the outstanding repair.
Or aesthetics: a minor marring of a building facade matters when it's pristine, but if you wait longer, the more other minor marrings appear, the less that first mar individually matters to the value of the building.
You don't always have the choice to delay. Sometimes the sublinear cost is associated with something you really need to do.
Examples of sublinear costs:
- When you pay lower per-unit price by ordering large lots. But you can't just wait a long time and be able to afford a giant order; your first small orders may help you generate revenue that let you buy the later big orders.
- When your team is young perhaps you need 1 new laptop per new hire. A few years later, you need < 1 new laptop per new hire b/c some receive recycled laptops from departed employees. But you can't wait 3 years and then hire 100 people and 80 laptops.
- You're growing some infrastructure which serves/covers some territory. At first, all new customers are in new territory; the ratio of new infrastructure to new customers is high. Later, some portion of new customers are covered by existing infrastructure, and that portion grows over time. But even if you could afford to build everything at once, you may not know where to build most of it until you have a bunch of customers.
- I don't really know if this one is true, but a whole industry seems to believe that a burst of advertising effort all at once is more effective than a marginally greater volume of advertising spread over a longer period. But you can't do zero advertising for 5 years and then take over time square.
Batching is an example of a sublinear cost. The more loads of laundry that I do at one time, the lower the total cost to do laundry. Unfortunately in this case, the smell in my closet goes superlinear, so it is rarely worth it ;)
Buying more groceries at a time is another obvious batching that one can do. So is cooking in bigger batches. Fixed cost in household chores is mostly about time, so you should try to batch them to save time. Buying groceries monthly vs. daily saves you 30 hours in a month, if it takes an hour for one trip.
24/7 365 support is a less obvious sub linear cost. At 4am one person can be vast overkill, but you can’t get away with zero people. The same is true as you keep scaling support, having level 1 vs level 2 is really about filtering problems ever more efficiently and having enough work where that’s actually needed at 4AM on Christmas takes scale.
Which is what economies of scale mean, just a huge range of sub linear costs all lumped together.
I think you’re misunderstanding the idea. Let’s use a simple model for N customers you need Pi people rounded up. As you increase by 10x the average cost per N customers keeps decreasing. So, f(N) = 4, f(10N) = 3.2, f(100N)= 3.15, f(1,000N)= 3.142 ...
Except in the real world this is being optimized based on a forecast at every point in the day and every day of the year. You might not think of demand in those terms, but it’s a huge area of optimization at both large companies like Walmart all the way down to individual restaurants.
You could also create combined call centers which service two industries which are similar enough, that the same support rep can serve them both, but have an opposite demand curve troughout the year.
To see these opportunities you need data, but it is the most closely guarded secret of companies.
In the short term it gives them an advantage, but in the long run society as a whole could benefit tremendously from open data sharing.
If you have a group of N potential customers that seek your services randomly and independently, then the standard deviation of customers is sqrt(N). This means you need less and less % extra capacity for handling randomly busy days the larger N is.
If you need to make changes to the design of an injection molded part, you'd better make them all at once, because the cost of a new mold is $100k whether you make one change or seven.
