The shape of the cost curves that show up as we build and run communications networks have properties that seem counterintuitive to many people, but that have been surprisingly consistent across lots of different technologies since at least the days of the telegraph, and probably further back than that.

Herewith, the Iron Laws of Network Cost Scaling:

1. Upgrade cost per increment of capacity decreases as capacity rises.

2. Network costs scale primarily with the number of troubleshooters required to run them, not with capacity.

3. Under market pressure, network pricing evolves from metered to flat-rate.

When you learn to apply all three of these together, you can make useful qualitative predictions across a surprisingly broad set of real-world cases.

The easiest way to see why upgrade cost per increment of capacity decreases as capacity rises is to think about the high capital cost associated with laying the first cable from A to B. You’re going to have to pay to dig a trench and lay conduit, or put the functional equivalent of telephone poles in a right-of-way. If we’re talking wireless, you need two antenna towers – OK, maybe one if the start end is on your net already. Trenches are expensive; rights-of-way and poles are expensive; towers are expensive.

But once you’ve got that physical conduit or poles or towers in place, pulling replacement wire or upgrading your radio repeaters is much less expensive. As your tech level rises, you (mostly) stop having to do that, even; you find cleverer ways to squeeze bandwidth out of fiber, copper, or air by using denser encodings, better noise cancellation – better algorithms. The action moves from hardware to software and upgrade costs drop.

As a very recent example of how the shift from hardware to software affects developing communications networks, the differences between the two major fourth-generation wireless data technologies, WiMAX and LTE, are so slight that the same hardware, running different software, can support either. This means that on any timescale longer than that required to push firmware upgrades to your repeaters, the differences between the two aren’t of consequence for planning.

The amortized cost of network capacity gets cheaper fast, partly due to the first Iron Law and partly due to the Moore’s Law cost curve of hardware. Skilled people on the spot don’t get cheaper. Therefore the dominant cost driver is salaries for people required to run the network. Furthermore, hardware/software maintainence costs tend to be low for the links (which are simple) and high for the switching nodes (which are complex).

The consequence is that cost scales not with network capacity but roughly with the number of routers and switches in the network, and is primarily salaries for people to watch and troubleshoot the routers and switches. This fact is well known to anyone who has ever had to actually run a data center or a network; it’s a reality that recurs very forcefully every time you have to pay the monthly bills.

There’s actually more we can say about this. In a roughly scale-free network (which communication networks with smart routing tend to become; it’s an effective way of maximizing robustness against random failures), the node count is coupled to the link count by about n log n. This means that as network reach or coverage (proportional to the number of leaf nodes, aka customers) rises linearly, the number of interior nodes (which counts routers and switches) actually rises sublinearly.

This is all in stark contrast with most peoples’ intuitions about network costs, which heavily overweight capital expenditures, heavily overweight bandwidth cost, and predict linear or superlinear rises in administrative costs as coverage increases (the “high-friction” model of network costs). But with a more correct model in hand, we can approach the third Iron Law: under market pressure, network pricing evolves from metered to flat-rate.

This is certainly the way network pricing has moved historically. Can we say anything generative about why this is so?

Yes, as a matter of fact, we can. We saw before that as customer count rises linearly, the major cost drivers in the network (router and switch count, and salaries for people to watch them) rise sublinearly. But to do per-transaction metering you have to store, manage, and process an amount of state that rises directly with customer usage – that is, linearly. This means that, especially on a maturing network, the cost to meter usage rises faster than the cost to serve new customers!

The qualifier “under market pressure” is important. Customers really don’t like being charged for both usage and maximum capacity. But they dislike being charged for usage more, because it makes their costs harder to predict (and usually higher). Comms providers are ruthless about exploiting the myth of high network friction to justify high prices and metering, and they generally get away with this for a while in the early stages of a new communications technology. But at least two things cooperate to change this over time, both actually effects of the widening gap between that mythical “high-friction” cost curve and the actual one.

One is that metering overhead rises as a proportional drag on on per-customer revenue (and thus profits) as the network’s coverage increases. Again, this is strictly predictable from the fact that the cost of service rises more slowly than the cost to meter. The other is that profit margins, as in any other sort of market, tend to get competed down towards actual cost levels. Telecomms vendors, like all other producers of non-positional goods, feel constant pressure to price in a way that actually matches their cost structure more closely.

Usually this means pricing by maximum capacity with no metering. Eventually, as link capacity reaches a level the average customer isn’t capable of saturating, flat-rate unlimited starts to make more sense.

Thus, communications-network prices have a very specific trajectory that’s repeated over and over with new technologies: from metered by transaction to billed by maximum capacity to flat-rate unlimited. The providers resist each change as ferociously as possible, because each one is accompanied by a shift to decreasing profit margins on increasing volume, but the underlying logic is inexorable.